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Integrated multiperiod power generation and transmission expansion planning with sustainability aspects in a stochastic environment
Abstract
This paper presents a multistage stochastic programming model to address sustainable power generation and transmission expansion planning. The model incorporates uncertainties about future electricity demand, fuel prices, greenhouse gas emissions, as well as possible disruptions to which the power system is subject. A number of sustainability regulations and policies are considered to establish a framework for the social responsibility of the power system. The proposed model is applied to a real-world case, and several sensitivity analyses are carried out to provide managerial insights into different aspects of the model. The results emphasize the important role played by sustainability policies on the configuration of the power grid.
... This involves exploring options for new transmission equipment alongside new generation units, known as generation and transmission expansion planning (GTEP). By optimizing the overall cost of the electrical system, including expansion and maintenance costs, along with transmission line constraints, this method facilitates the simultaneous location of new generation units and the construction of new transmission lines [8]. ...
... Therefore, some publications also focus on environmental aspects. Seddighi and Ahmadi-Javid [8] proposed a multistage stochastic programming model to address sustainable power GTEP. Moreira et al. [20] proposed a two-stage min-max-min model for co-optimizing the expansion of the transmission system and renewable generation capacity. ...
... Scholars presenting GEP or GTEP methods with multi-year models often rely on existing load forecasts or assume that demand at each bus changes at the same rate [9], [12], [14], [17], [18], [25], [26], [28], [30], [37], [38]. Regarding network data, they typically assume that the number of buses in the future transmission network remains the same as the current one, as seen in [7], [8], [9], [10], [13], [14], [16], [17], [18], [21], [22], [25], [26], [27], [28], [29], [30], [31], [35], [36], [37], [38], [39], and [41]. Alternatively, they may predefine new buses in advance, as shown in [11], [12], [15], [17], [20], [23], [30], and [40]. ...
A typical power system consists of a network of generation, transmission, and load spanning a wide area, with varying spatial characteristics crucial for realistic problem-solving. Traditional generation expansion plans (GEP) often treat the generation system as a single area, specifying types and sizes of new power plants to meet future demand without designating specific locations. This approach leads to generation-demand imbalances, unnecessary transmission expansions, and other issues. Incorporating regional constraints like local demand, tie-line capacity, and available resources is essential in GEP. Considering the entire transmission network can address spatial characteristics but presents challenges due to extensive data preparation and computational complexity. This paper proposes a GEP approach that accounts for spatial characteristics such as primary energy sources, renewable energy potential, and feasible locations for future units. With the proposed method, the power system is divided into multiple zones, each represented by a single bus connected by interzonal transmission lines. This zonal approach simplifies the transmission model by focusing on interzonal data, making it more practical for actual power systems. An area-based reliability index is used to evaluate each area’s reliability level, aiding in the suitable placement of future generation units. The proposed method was tested using Thailand’s latest power development plan, PDP2018 revision 1. Results show that accounting for spatial characteristics alters the generation expansion plan. Additionally, new units are distributed across the system to maintain area reliability. Improved computational efficiency of this proposed method allows for addressing uncertainty by solving multiple scenarios with varying input data and probabilities.
... References [4][5][6][7] present extensive reviews of the existing literature on GEP and related aspects. The GEP problem can be considered independently [8] or in tandem with other objectives such as transmission expansion planning (TEP) [9][10][11][12][13][14][15]. Furthermore, GEP methodologies are often employed to determine the impact of a new technology and/or system development. ...
... The work in Ref. [10] initially formulates the GEP and TEP optimisation problems in a static and multi-level planning framework, but with the capability of performing year-by-year dynamic analysis. Other approaches include the sequential static planning or rolling horizon approach, which can be adopted when the planning horizon is long [13]. However, strict differentiation between dynamic and static planning is not possible as they are not clearly defined in the literature. ...
... The main finding of this analysis is that scaling up offshore wind fares well with stakeholder preferences. Similarly, the work in Ref. [13] presents a joint network and generation expansion model, incorporating various sustainability aspects and climate policies. Other notable works along this line include [39,40]. ...
Electricity generation capacity expansion is driven by both economic and socio-political realities. Policy makers determine public infrastructural decisions, such as climate and renewable targets, and transmission infrastructure, and the optimal generation capacity expansion follows. Policy makers therefore require planning models that can determine the optimal generation capacity mix in the long run under various scenarios, including policy choices. This work presents a planning model based on linearised alternating current optimal power flow which determines optimal generation capacity expansion and operation, in a least-cost manner, given global and local technical constraints, as well as policy decisions. We apply the model to a test case of the island of Ireland, which has two weakly interconnected systems, high renewable generation targets and low storage and interconnection. We determine the optimal generation expansion and operation out to 2030 considering the effects of increased multi-area interconnection, existing fossil fuel generation phase-out and increased renewable generation targets and carbon prices. Our results find that costs and emissions are driven primarily by the decommissioning of old inefficient generation units. High renewable targets, on the other hand, render increased carbon prices relatively ineffective in reducing system emissions. Furthermore, high renewable generation targets crowd out low-carbon power generation options such as carbon capture and storage (CCS). The strategic north-south interconnection has little effect on renewable energy source installations required to achieve renewable power generation targets but does impact on security of supply and the congestion level across the island.
... The work in [10] initially formulates the GEP and TEP optimization problems in a static and multi-level planning framework, but with the capability of performing year-by-year dynamic analysis. Sequential static planning or a rolling horizon approach is adopted when the planning horizon is long [13]. Strict differentiation between dynamic and static planning is not possible as they are not clearly defined in the literature. ...
... Some studies have also included reliability issues in a GEP framework, for example [9], which considers generator and transmission line outages in a GTEP optimization. Authors in [13] perform an in-depth stochastic GTEP exercise with uncertainties in demand, fuel prices, costs of greenhouse gas emissions and supply disruptions. ...
... Penalties associated with unserved power are set as ,ℎ = 3000 €/ and ,ℎ = 3000 €/ according to [40]. These penalties can be regarded as rather conservative assumptions; lost load is in fact valued 13 much higher [53]. The interest rate is set to 10%. ...
Increased renewable generation worldwide is posing new challenges for power system planners. The location, as well as the level and operation, of each generation resource is increasingly important. This work presents a constrained Generation Expansion Planning (GEP) optimization model. One of the salient features of the model is its reasonably accurate representation of the physical characteristics of power systems. It considers both active and reactive power flows in a linear manner. Natural voltage magnitude deviations from nominal values across the transmission system are also captured in the resulting model. Therefore, the network model employed here closely resembles the AC optimal power flow one. We apply the model to a realistic test system of the island of Ireland and determine the optimal generation expansion and operation out to 2030 under a range of demand and policy scenarios. Our results show that costs and emissions are driven primarily by the decommissioning of old inefficient generation units. High renewable targets, on the other hand, render increased carbon prices relatively ineffective in reducing system emissions.
... Conventionally, power system planning refers to generation expansion planning (GEP) and transmission expansion planning (TEP) [1]. GEP copes with the planning of generation capacity expansion, while TEP solves the problem of transmission grid development [2][3][4]. ...
... Some researchers have done meaningful work in this area. Seddighi and Ahmadi-Javid put forward a multi-stage planning model to balance sustainable GEP and TEP [1]. Aghaei et al. propose a probabilistic model for generation and transmission expansion planning considering reliability criteria [7]. ...
Under the environment of energy internet, various elements in power source side, transmission grid side and load demand side should be considered in power system planning issues. Besides, as variable new energy generation and demand response will play increasingly significant parts in power systems, there should be more interactions between power system planning and operation simulation, since the characteristics and impacts of these new elements can only be effectively reflected in the operation level. Accordingly, a novel planning approach incorporating operation simulation is proposed in this paper. The model consists of two levels, source-grid-load coordinated planning level and operation simulation level. The interactions between the two levels ensure that a reasonable planning result can be found. The case study tests the feasibility of the novel planning approach.
... Seddighi and Ahmadi-Javid solve the generation expansion planning problem with transmission expansion decisions, while incorporating uncertainties about future, as well as potential disruptions. Their work also considers various sustainability regulations and policies that are evaluated for the Iranian market [25]. Koltsaklis and Georgiadis evaluate the impact of CO 2 emissions pricing in the context of GEP by including unit commitment constraints into the model [26]. ...
... Upon experimentation in COBRA, this collection of data points is then appended to the original experimental data set, and the metamodel is refit. Equation (25) defines the expected improvement function, E½IðxÞ. ...
Electricity generation expansion planning models determine the optimal technology-capacity-investment strategy that minimizes market costs including investment costs, and fixed and variable operating & maintenance costs over a long-term planning horizon. From a market cost perspective, fossil fuels are among the most economical sources of electricity, and thus are the primary sources of energy for electricity. However, these energy sources create by-products that have harmful health effects upon exposure. In this paper, a simulation-based, metamodeling approach is leveraged to quantify health damages associated with power grid expansion decisions by linking the outputs of generation expansion planning simulations with a screening tool that quantifies the human health damages from the electricity sector. Using this as a surrogate function for health damages, these costs are included in the objective function of a generation expansion planning model, in addition to market costs and the social damages of carbon emissions and methane leakage to minimize societal damages. Applying an improvement algorithm, candidate data points are selected to enhance metamodel prediction capability. Finally, using an updated metamodel, a new expansion plan is found. This framework enables researchers to better understand the health implications of long-term capacity expansion decisions.
... In Ref. [18] a multiperiod model for generation and transmission expansion is developed which focuses on environmental aspects, and in Ref. [19] the problem in a renewable energy environment is considered. Finally, a general algorithmic framework for the generation and transmission expansion optimization problem is developed in Ref. [20]. ...
... Constraints (15)e (20) are integer and non-negativity constraints. Constraints (15) through (18) define the domain of the infrastructure upgrade decision variables, and are therefore binary. Constraints (19) and (20) are used for the flow and generation problem, and are therefore continuous non-negative. ...
One of the challenges in electrical grid expansion planning is how to expand the infrastructure while considering fundamental changes in demand and supply, in part due to “game-changing” consumers, such as electric vehicles (EVs), and optional distributed generation (DG) by consumers. This work proposes an optimization model that addresses the generation and transmission expansion of the grid, including the facilities’ locations, upgrades, and the network's design decisions. In contrast to some other models, it is not static in time: the model considers time-dependent demand in short-term (hourly) and long-term (yearly) variations. The proposed optimization model considers energy loss, transmission substations upgrades, constraints such as demand, capacities, and more. The model minimizes the long-term costs of infrastructure investments and the operational costs of generation. The work is supplemented by numerical experiments of the model in simulated scenarios. Sensitivity analysis conducted on some of the model features, demonstrates the importance of including them in the model.
... • Rainfall and hydropower siting [113] • Transformer siting and installed capacity planning [116] • The effect of the N-1 criterion on TEP [99] • HVAC and HVDC collaborative planning using DCOPF and ACOPF models [138] • Co-planning of Electric Vehicles (EVs) and distributed grids with TEP [100], [120] 12 VOLUME 11, 2024 This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. ...
Transmission Expansion Planning (TEP) is a well-established field in power systems, focused on identifying the best timing and location for new transmission lines and related infrastructure. Its main goals are to meet growing electricity demand, ensure system reliability, and maintain economic efficiency. However, recent changes in the energy sector – such as the rapid growth of renewable energy, the push for decarbonisation, and the rise of electric vehicles – have introduced new challenges and uncertainties for TEP. This paper reviews more than 150 research articles to explore how these trends are reshaping TEP. We identify key insights, emerging challenges, and research gaps, emphasizing the need for improved tools and approaches to address the complexities of modern power systems. Finally, we discuss the need for research in TEP to incorporate uncertainties like energy storage systems (ESS), electric vehicle adoption, and high renewable energy integration, using advanced algorithms and real-world data to enhance accuracy and relevance.
... Improved innovative algorithms have been used to solve the problems associated with the power system planning. In [41,13] market-based TEP has been solved in the form of a complex mixed integer non-linear programming (MIP) with improved differential evolution algorithm. The main goal is to minimize global production and transmission costs for the participants in the market. ...
... Reference [179] proposes a minimumemission optimal dispatch by adding emission cost to the dispatch cost function. This approach has been applied to transmission planning and joint generation and transmission expansion to minimize renewable energy spillage in the long term [180,181]. However, including emission costs in the objective function is not favored by current industry practices since it artificially increases total dispatch cost in the system and leads to inefficient economic signals for investment. ...
The electric power sector is experiencing a rapid shift towards renewable energy resources. To accommodate this transition, the transmission system requires significant upgrades. Although enhancing grid capacity through transmission network expansion is always a solution, it can be very costly and requires a protracted permitting process. Grid-enhancing technologies (GETs)encompass a broad range of hardware and software tools that enable reconfiguration of the transmission grid and adjustment of its parameters. The proliferation of such technologies enhances transfer capability over the current transmission network, thus reducing the need for grid expansion. This paper offers a comprehensive review of grid-enhancing technologies. The paper discusses definitions of transmission flexibility and presents methods that are developed to quantify grid flexibility. The paper offers a comprehensive review of an extensive range of grid-enhancing technologies, including both principles of operation and state-of-the-art developments. Environmental impacts of grid-enhancing technologies, including renewable energy curtailment and carbon emission reduction, are also discussed. In addition to technical aspects of grid enhancing technologies, the paper also reviews the literature on the incentive challenges facing GETs adoption and efficient. Overall, the paper offers a comprehensive review of the advancements in grid-enhancing technologies, discusses the remaining critical challenges, and identifies directions for future research.
... Reference [147] proposes a minimum-emission optimal dispatch by adding emission cost to the dispatch cost function. This approach has been applied to transmission planning and joint generation and transmission expansion to minimize renewable energy spillage in the long term [148], [149]. However, including emission costs in the objective function is not favored by current industry practices since it artificially increases total dispatch cost in the system and leads to inefficient economic signals for investment. ...
As global concerns regarding climate change are increasing worldwide, the transition towards clean energy sources has accelerated. Accounting for a large share of energy consumption , the electricity sector is experiencing a significant shift towards renewable energy sources. To accommodate this rapid shift, the transmission system requires major upgrades. Although enhancing grid capacity through transmission system expansion is always a solution, this solution is very costly and requires a protracted permitting process. The concept of flexible transmission encompasses a broad range of technologies and market tools that enable effective reconfiguration and manipulation of the power grid for leveraged dispatch of renewable energy resources. The proliferation of such technologies allows for enhanced transfer capability over the current transmission network, thus reducing the need for grid expansion projects. This paper comprehensively reviews flexible transmission technologies and their role in achieving a net-zero carbon emission grid vision. Flexible transmission definitions from different viewpoints are discussed, and mathematical measures to quantify grid flexibility are reviewed. An extensive range of technologies enhancing flexibility across the grid is introduced and explored in detail. The environmental impacts of flexible transmission, including renewable energy utilization and carbon emission reduction, are presented. Finally, market models required for creating proper incentives for the deployment of flexible transmission and regulatory barriers and challenges are discussed.
... There are multiple social and economic benefits from expanding energy systems and improving energy access [1]. Yet building new energy systems is often constrained by physical resources, budgets, and environmental concerns [2]. Overcoming these challenges in a timely manner can be difficult for energy system planners, especially when demand is uncertain. ...
A classic multi-period stochastic energy system expansion planning (ESEP) model aims to address demand uncertainty by requiring immediate demand satisfaction for all scenarios. However, this approach may result in an expensive system that deviates from the planner’s long-term goals, especially when facing unexpectedly high demand scenarios. To address this issue, we propose a chance-constrained stochastic multi-stage ESEP model that allows for a portion of demand to remain unmet in specific periods while still ensuring complete demand satisfaction during most of the planning horizon, including the final period. This approach provides more time flexibility to build infrastructure and assess needs, ultimately reducing costs and allowing for a broader view of infrastructure planning options. To solve the chance-constrained stochastic model, we introduce a binary-search-based progressive hedging algorithm heuristic, which is particularly useful for large-scale models. We demonstrate the effectiveness and benefits of implementing the chance-constrained model through a case study of Rwanda using real-world data.
... Meanwhile, reliability [7][8][9], environmental protection policies, and other factors are also considered when solving the power planning problem. In addition, the traditional GEP problem is also combined with other long-term planning problems [10][11][12], such as generation and transmission planning problems [3,4,13]. ...
With the continuous promotion of China’s electricity market reform, the introduction of competition in the power generation market provides a new research direction for the generation expansion planning (GEP) problem, which is of great significance in the promotion of the optimization of the power energy structure. In the context of marketization, the electricity price expectation during the planning period is a key factor of GEP for independent power generation groups. There is some literature showing that the electricity price expectation in the planning period can be estimated according to certain laws of market supply and demand, while it seems to us that a future Pay as Bid (PAB) mechanism is better to determine the electricity price expectation. In this paper, to explore the impact of these two different electricity price formation mechanisms on the evolution of the generation market, a multi-agent framework is first established to describe the interaction process among the generation market agents; then, a GEP model for independent power generation groups is developed in the market competition environment, and four representative scenarios are finally designed for detailed comparative studies. Based on these case studies, the conclusion can be summarized as: (1) the PAB bidding mechanism has a lower electricity price and higher market installed capacity almost all the time during the whole planning period for all four scenarios; (2) it is more important that PAB can reduce the impact of parameter uncertainty in the laws of market supply and demand, which can obtain more reliable and reasonable results regarding the long-term evolution of the generation market.
... With respect to the weighted sum approach, it shows that by using a new solution method, lower total cost and lower total carbon emission under the same total power generation are obtained. In ref. [17] a multi-stage stochastic generationtransmission expansion planning model (GEP-TEP model) is introduced in which different uncertainties of a future power system such as load demand, fuel prices, and greenhouse gas emissions are considered. In addition, sustainability policies, including noise pollution and social expectation, consider the power system social responsibilities. ...
The operational flexibility of electric energy systems is one of the essential requirements for integrating a high share of renewable resources. The operational flexibility significantly impacts the mix of new power generation technologies. In this paper, a low‐carbon generation expansion planning (GEP) model is presented to investigate the impacts of flexibility requirements of power systems with a high share of wind energy. An improved clustered unit commitment (CUC) formulation is proposed to capture the flexibility limitation of thermal generating units fully. In this regard, clustered 10‐min ramp up/down limits for operational reserves, flexible‐ramp reserves, and contingency reserves, are introduced. The yearly variations of load and renewable generations preserving the chronological time correlations are included, considering 36 representative days obtained by the clustering approach. Besides, two types of BES devices are considered to investigate the role of BES in the provision of flexibility. The proposed flexible low‐carbon GEP model is formulated as a mixed‐integer programming model, and an optimal expansion plan is obtained using the CPLEX algorithm. By incorporating improved CUC formulation into the low‐carbon GEP model, more profound insight into power systems' flexibility requirement with high wind generation penetration is obtained.
... Los modelos estáticos [3,14-19,23] desarrollan el análisis para un solo año de referencia, que por lo general es el último año de planificación. Por otra parte, los modelos dinámicos [1, [20][21][22]24] o multietapa aplican criterios de decisión de expansión en diferentes periodos de tiempo. ...
La energía eléctrica juega un rol importante y continuo en el desarrollo de la economía, industria y la sociedad. La planificación del desarrollo de la industria eléctrica es importante para contar con un suministro de energía asequible, fiable y oportuna. Uno de los temas ampliamente estudiado y aplicado es la modelación matemática para la planificación eléctrica.
En este artículo se formula y desarrolla un modelo multietapa para planificar la expansión óptima de la generación y transmisión a largo plazo en el Perú. La metodología para el desarrollo del modelo se basa en técnicas de programación lineal entera mixta y cuyo modelo matemático ha sido validado con modelos similares publicados en artículos científicos recientes.
La importancia de este artículo radica en demostrar, a diferencia de casos teóricos de investigaciones previas, la aplicación del modelo para el caso de la expansión de la generación y transmisión en el Perú. El objetivo de este trabajo es evaluar y determinar la oportunidad de ingreso de los proyectos de generación y transmisión para para lograr una expansión óptima del parque de generación y transmisión en el Sistema eléctrico Interconectado Nacional (SEIN) en el horizonte 2017 – 2040.
... For example, the work in Pozo et al. (2013) is originally formulated under a static planning framework, but with the capability of performing year-by-year dynamic analysis. Others resort to sequential static planning or the rolling horizon approach, especially when dealing with long-term planning horizon (Seddighi and Ahmadi-Javid, 2015). Despite an extensive literature on power system planning, network and spatial effects are largely neglected or insufficiently accounted for in existing models (Zhang et al., 2019;Guerra et al., 2016). ...
This paper analyses how people’s attitudes towards onshore wind power and overhead transmission lines affect the cost-optimal development of electricity generation mixes, under a high renewable energy policy. A power systems generation and transmission expansion planning model is used for the analysis, combined with a novel additional modelling constraint incorporating public acceptance of energy infrastructure. In the scenarios examined the least cost solutions increase by as much as 33% compared to a base case where the constraint on public acceptance of energy infrastructure is excluded. In the most extreme public acceptance scenario considered, the greatest share of additional costs (>80%) is related to value of lost load, while additional investment and operational costs associated with public acceptance constraints for new energy infrastructure are between 5–6% of base case costs. The results are indicative of the cost that power systems face in reflecting the public’s preferences for new energy infrastructure in generation and grid expansion planning. Power system modelling that ignores the public’s acceptance of new energy infrastructure may offer generation or transmission pathways that are likely to be sub-optimal in practice.
... QO "CU= xOQm ?rH OwN x@ = Q u= R=Us}tYD w |v=yH |xat=H xHwD xm CU= Qo}Om} Q=vm QO |]}LtCU} R w |a=tDH= '|O=YDk= Oa@ xU x@ xHwD 'OQm}wQ u}= p=Ut wQ u}= R= "OW=@ QDQ=O}=B |}xaUwD w QDy@ |}xOv}; |xOvvmu}t[D Ov=wD|t u}vJ R= |Q}oxQy@ OvtR=}v R}v jQ@ p=kDv= w O}rwD |xaUwD |R}Qxt=vQ@ uwJty |tyt |y=ov =@ = Q xRwL u}= QO xv=y=o ; w |tra |Q}os}tYD Ovv=wD@ =D OvDUy |R=Ov=sWJ "Ovvm s=eO= Q=O}=B |xOWQWDvt C=k}kLD R= |m} QO Q[=L |xr=kt u=oOvU}wv =DU=Q u}= QO \}=QW QO jQ@ |xm@W p=kDv= w O}rwD Q=O}=B |xaUwD |R}Qxt=vQ@ |= Q@ [1] OwN |r@k |vD@t xr=kt u; QO xOWx= Q= pL x= Q =t= "OvOw@ xOQm x=Q= = Q |at=H pOt C}a]ksOa u; \UwD pL =} q=@ O=a@= QO xm CU= |R=Uxv}y@ |=yQ= Ri=sQv R= s}kDUt |xO=iDU= Q@ xr=kt u}= '`w[wt u}=`iQ Qw_vtx@ "OwW|t |vqw] Q=}U@ pL u=tR =} CU= umtt=v "OyO|t x=Q= q=@ O=a@= QO xJQ=Bm} pOt u}= pL |= Q@ QFwt Q=}U@ pL sD} Qwor= l} u; pL |= Q@ w CU= |}xrLQtOvJ |iO=YD |R}Qxt=vQ@ pOt l} |UQQ@ OQwt pOt |}=Q=m xm OW Oy=wN O=yvW}B xOW`}QUD RQOv@ |x}RHD VwQ Q@|vD@t |tD} Qwor= |xaUwD QO xOvvm`}QUD w |QwQ[ |Wkv 'CQOk u=v}t]= p@=k p=kDv= w O}rwD Q=tW x@ CQOk sDU}U C} Q}Ot QO |rY= |=yxeOeO R= w OQ=O QwWm |O=YDk= |O=}R Q}F -=D xm OQ}o|t Q@ QO = Q |irDNt |=yx@vH CQOk p=kDv= w O}rwD "O};|t |oOwr; '=wy |oOwr; '|}xv=Npo |=yR=o Q=WDv= uwJty CU} R \}Lt w xat=H Q@ xOvvmpDNt EO=wL xm |t=ovy C= Q}F -=D u}= "OQ=Po|t |a=tDH= C= Q=_Dv= w |DwY 'xwqax@ "OvwW|t R}v QDO}OW |DL OvDi=|t j=iD= |v=Uv= w |a}@] Kv=wU Ovv=t |R}Qxt=vQ@ Q@ R}v CNwU Ct}k w jQ@ xOv}; |=[=kD x@ \w@Qt C}a]ksOa CUO x@ = Q |Oa@ OvJ w xO}J}B |xrUt l} xH}Dv QO w CU= Q=PoQ}F -=D "OyO|t Q]N x@ uwO@ RwQt= |=yR=}v uOQm hQ]Q@ p=@vO x@ xm Q=O}=B |xaUwD Qo}O hQ] R= |D=aw[wt Qo}O R= 'CU= u=WR=}v |R=UhQ]Q@ |= Q@ xOv}; pUv |}=v=wD uDN=Ov= pwUt xOvU}wv "1398 10 29 VQ}PB '1398 10 2 x}LqY= '1398 5 23 Ci=} QO %M}Q=D """ |R}Qxt=vQ@ |= Q@ xOW`}QUD RQOv@ |x}RHD VwQ w CO 2 R=o C= Q=WDv= uDiQo Q_v QO =@ = Q GEP [13] u= Q=mty w = Rt "CU= xOW \wrNt K}LYOOa |]NQ}e |R}Qxt=vQ@ pOt l} ?r=k QO CNwU Ct}k lU} Q uOQm^=Lr =@ [14] u= Q=mty w swm}Q}U "OvOQm |Ov@pwtQi |}xQwOlD xiOyOvJ w SO 2 ) =wy |=yxOv}q; Q=WDv= |= Q@ |}=yC}OwOLt w |]}LtCU} R |=yxv}Ry |=ypt=a C= Q}F -=D [15] u= Q=mty w uJ "OvO=O xaUwD = Q GEP |xrUt (PM 10 |= Q@ |ki=wD |Ov@pOt OQm}wQ l} w OvOQm ?}mQD ...
این مقاله الگوریتم مؤثری برای حل یک مدل برنامهریزی تصادفی چندمرحلهیی مورد استفاده در برنامهریزی یکپارچهی توسعهی شبکهی انتقال و تولید یک سیستم قدرت با عدمقطعیت در تقاضای آیندهی برق، قیمتهای سوخت، انتشار گازهای گلخانهیی و با در نظر گرفتن ریسک اختلال در ظرفیت تولید و انتقال ارائه میکند. در مدل مذکور هر سه جنبهی اقتصادی، اجتماعی و زیستمحیطی پایداری در تولید و انتقال برق لحاظ شده است. الگوریتم ارائهشده با استفاده از ساختار ویژهی مدل بر مبنای تجزیهی بندرز طراحی شده است و روشهایی برای تسریع آن به کار گرفته شده است. نتایج محاسباتی نشانگر کارایی قابلقبول الگوریتم پیشنهادی است. سپس نتایج برای مطالعهی موردی در شبکهی برق شمال غرب ایران به کارگرفته شده است. این بررسی عددی به خوبی نشان میدهد که یکپارچهسازی انتقال و تولید سیستم قدرت میتواند منجر به اتخاذ تصمیمات پایدارتری از وجوه مختلف در طول دورهی برنامهریزی شود.
... River basin-specific information could be incorporated into the model with input for lifespans and operational costs of existing dams for a more accurate representation of existing infrastructure. Basinscale detail could penalize projects in remote locations, as longer transmission lines often contribute to higher cost overruns (Sovacool et al., 2014) by introducing additional financial, environmental, and social risks (Seddighi and Ahmadi-Javid, 2015). ...
Hydropower accounts for approximately 60% of electricity generation in Canada, with growth expected in the coming decades as part of renewable energy transitions; however, frequent cost overruns threaten the viability of this growth. Using the integrated assessment model GCAM, we develop an endogenous representation of hydropower for Canada that accounts for market dynamics, thus permitting analysis of hydropower competition with other electricity generation technologies, both with and without cost overruns. Results show that modelling hydropower resources endogenously increases Canadian hydropower deployment relative to an assumption of fixed hydropower production, from 417 to 495 TWh annually by 2050. In scenarios that apply cost overruns at historical levels, hydropower loses market share to more easily scalable technologies like wind power. When including high cost overrun assumptions, the model determines that hydropower falls from about 73% to 65% of Canadian electricity generation by 2050, while wind power increases from about 8% to 11%. Countries may be better able to achieve electrification and renewable energy targets at lower cost by avoiding large-scale, overrun-prone hydropower and nuclear generation projects. Model results support that cost overruns are important considerations for policy decisions related to electricity sector development in Canada and elsewhere.
... A sustainable approach considering an integrated multi-period model for generation and transmission expansion planning problem has been addressed in [7] to consider the socio-environmental impacts of the power system, including greenhouse gas emissions, noise pollution and social expectations. In addition, a bi-level clustering framework based on objective-based scenario selection has been addressed in [8]. ...
Integrated transmission expansion planning (TEP) and generation expansion planning (GEP) with Wind Farms (WFs) is addressed in this paper. The optimal number of expanded lines, the optimal capacity of WFs installed capacity, and the optimal capacity of wind farms lines (WFLs) are determined through a new TEP optimization model. Furthermore, the optimum capacity additions including conventional generating units is obtained in the proposed model. The Benders decomposition approach is used for solving the optimization problem, including a master problem and two sub-problems with internal scenario analysis. In order to reduce the computational burden of the multi-year and multi-objective expansion planning problem, a multi-stage framework is presented in this paper. The uncertainties of wind speed and system demand along with contingency scenarios lead to a probabilistic optimization problem. Moreover, in the proposed model, the planning time horizon is divided into three predefined stages. This multi-stage approach is used to increase the proposed model accuracy in a power system with a high level of wind power penetration. Hence, in this paper a scenario-based probabilistic multi-stage model for transmission expansion planning is proposed, incorporating optimal WFs integration. It is recognized that high wind penetration increases the transmission expansion investment cost, but based on the reduction of the investment cost of conventional units, the total system cost will be smaller. This result emphasizes the main advantage of wind generating system over the conventional generating system. This planning methodology is applied to the modified IEEE 24-bus test system and simplified Iran 400-kV real system to show the feasibility of the proposed algorithm.
... The cost of electricity generation evaluated independently as an indicator of sustainability (Newton and Hopewell, 2002). The avoided emission is considered an essential indicator for sustainability evaluation of electricity generation (Pan et al., 2018), while impacts on the natural resources represented by water and land were overlooked (Seddighi and Javid, 2015). Sustainability indicators were used in some earlier studies to rank the different power generation technologies. ...
The principal objective of this paper is to develop an optimization model to integrate the available natural resources in a specific jurisdiction and to find the most sustainable power generation pathways to face the electricity demand with the lowest possible environmental and economic impacts. The sustainability was assessed in this paper quantitatively through environmental, economic, and natural resources indicators. A linear programming model was constructed with the objective function to minimize the cost of power generation and constrained by the available natural resources and greenhouse gas (GHG) emissions in a specific jurisdiction. Constraints for the model were represented by electricity demand, water consumption, water withdrawals, emitted CO 2 , and land area needed for power generation. The business-as-usual scenario was based on the existing power generation mix of Alberta Province in Canada and compared to the other four scenarios conducted through the optimization model. Cost-Effective, sustainable, stringent, and renewable penetration scenarios were conducted. Compared to the business-as-usual scenario, the cost-effective scenario resulted in a less average levelized cost of electricity (LCOE AV) by 17%, higher by 41% for the sustainable scenario, and higher by 14% for the stringent scenario.
... Different models have thus far been proposed for the composite GEP and transmission expansion planning (TEP) problem, such as static models [6][7][8] and dynamic models [9][10][11]. Besides, some models are single-objective [12][13][14] while some of them are multiobjective [15][16][17][18]. Asensio et al. [19] proposed a sustainable expansion planning in distribution systems and Bagheri et al. [20] focused on increasing the renewable energy contribution and ultimately, implementing 100% renewable energies. ...
Recently, demand response programmes (DRPs) have captured great attention in electric power systems. DRPs such as time-of-use (ToU) programme can be efficiently employed in the power system planning to reform the long-term behaviour of the load demands. The composite generation expansion planning (GEP) and transmission expansion planning (TEP) known as composite GEP–TEP is of high significance in power systems to meet the future load demand of the system and also integrate renewable energy sources (RESs). In this regard, this study presents a dynamic optimisation framework for the composite GEP–TEP problem taking into consideration the ToU programme and also, the incentive-based and supportive programmes. Accordingly, the performances of the capacity payment and feed-in tariff mechanisms and the ToU programme in integrating RESs and reducing the total cost have been evaluated in this study. The problem has been formulated and solved as a standard two-stage mixed-integer linear programming model aimed at minimising the total costs. In this model, the ToU programme is applied and the results are fed into the expansion planning problem as the input. The proposed framework is simulated on the IEEE Reliability Test System to verify the effectiveness of the model and discuss the results obtained from implementing the mentioned mechanisms to support the RESs integration.
... Regarding the mathematical formulation for the coordinated generation and transmission expansion planning, GTEP problem, it should be noticed that it is a complex optimization task that has nonlinear, non-convex and mixed-integer nature. As examples, this problem was addressed in [121,[125][126][127][128][129][130]. ...
Transmission Expansion Planning (TEP) problem aims at identifying when and where new equipment as transmission lines, cables and transformers should be inserted on the grid. The transmission upgrade capacity is motivated by several factors as meeting the increasing electricity demand, increasing the reliability of the system and providing non-discriminatory access to cheap generation for consumers. However, TEP problems have been changing over the years as the electrical system evolves. In this way, this paper provides a detailed historical analysis of the evolution of the TEP over the years and the prospects for this challenging task.
Furthermore, this study presents an outline review of more than 130 recent articles about TEP problems, literature insights and identified gaps as a critical thinking in how new tools and approaches on TEP can contribute for the new era of renewable and distributed electricity markets.
... The obvious economic advantages of an integrated model over a separated planning were proven in [20][21][22] by analyzing the IG-TEP model. The authors presented a multistage stochastic programming model of IG-TEP in [23] to address sustainable problems and the uncertainties of future electricity demand, fuel prices, and greenhouse gas emissions to which the power system was subjected. A linear optimization model was built in [24] to define cost-optimal pathways toward a sustainable power system in the Association of East Asian Nations (ASEAN), and the results suggested to foster a diversified extension of renewables and elaborating on political and technical solutions. ...
The government of China has introduced a series of energy-saving and emission reduction policies and energy industry development plans to promote the low-carbon development of the power sector. Under relatively clear and specific low-carbon development goals, the ongoing power transition has recently been studied intensively in the frame of global sustainable transition. With the development of renewable technologies, besides the long-term development goals, learning and diffusion of innovative technologies and the incentive effect of supportive policies are also important driving forces of the transition. The levelized power generation cost is the power generation cost when the net present value of the power project is zero. In this paper, the levelized power generation cost model with a learning curve and policy scenario is used to reflect the impact of technology diffusion and incentive policies from the economy perspective. By treating it as a state transfer function, a dynamic power generation–transmission integrated planning model based on the Markov Decision Process is established to describe the long-term power transition pathway under the impact of power technology diffusion and incentive policies. Through the calculation of power demand forecasting data up to 2050 and other power system information, the dynamic planning result shows that the current low-carbon policies cannot obviously reduce the expansion of coal power, but if strict low-carbon policies are implemented, the renewable power will gradually become dominant in the power structure before 2030.
... (21) states that the power generation by units comprising both existing and new installed generating units. The total load at each bus at each time is calculated as Eq.(22). The generation and demand balance is addressed in Eq.(23)and Eq. (24) shows the power flow of lines taking into account the new configuration of the transmission network in the case of newly installed lines. ...
A multi-objective wind farm integration framework is proposed in this paper which considers the composite generation and reliability assessment and annualized operating and investment cost evaluation. An emission-controlled policy is adopted such that the amount of SOx and NOx decreases in line with renewable resource planning. Since the incorporation of large-scale distant wind farms is a problem of the multi-objective mixed-integer type with nonlinearities and non-convexities, this paper utilizes a fast elicit multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA II) by probabilistic indices. It is noted that the impacts of the unavailability of the transmission system are modeled employing DC Optimal Power Flow (OPF) based on the incidence matrix together with the static security evaluation. Furthermore, in order to assess the performance of the suggested approach, the model is implemented on the Roy Billinton Test System (RBTS). Afterwards, distant wind farms integration into Iran's South-West Regional Grid (ISWRG) is studied.
... Due to the randomness and uncertainty in the calculation of demand forecasting where data is missed, we consider demand as a random variable and utilize stochastic selection approach. To handle the uncertainty, for VOLUME 6, 2018 checking the network capacity violation, MCS based sampling of demand scenarios is deployed here which is one of the standard methods [15], [52], [53]. The adopted approach is recommended in recent empirical studies for the robust design of the network which incorporates the maximum demand scenarios to test the security of planned network against various contingency scenarios. ...
In this paper, a real-life application of bi-level evolutionary optimization is proposed to optimize the electricity industry infrastructure. It offers a coordinated generation and transmission expansion planning (CGTEP) from the perspective of Independent System Operator (ISO). The main objective of the proposed study is to show the effect of optimizing the generators concerning capacity and location both to reduce the transmission investment and increasing the reliability of the network. The proposed framework of bi-level optimization contributes to utilize global evolutionary optimization method GA in its hybrid form in level-I to select the location of lines and energy generators. The respective capacities of the corresponding selected lines and generators are optimized in the level-II by RW. In conflicting objectives of minimizing the investment for capacity addition in the network and maximizing the reliability, a Pareto-optimal solution is achieved by using the Theory of Marginal Value (TMV). To satisfy TMV, the total cost is minimized, which comprises the cost of investment in building new transmission and generation capacities, cost of not-served expected energy, cost of unutilized expected generation, and cost of unserved energy due to the constrained network. Proposed methodology on IEEE 24-bus power system is presented encountering the combination of N-1 and probable N-2 contingency security criteria. The comparison results show that bi-level GA-RW optimization minimizes the investment with increasing power system reliability.
... Equation (10) states that the cost includes generators (power plants) and wind units re-dispatching considering lines limit as well as pollution penalty [14]. The pollution cost has to be paid by [15]. ...
In this study, a transmission expansion planning problem is studied considering the effects of wind units on transmission congestion and power system reliability in a uniform electricity market. Market clearing price (MCP) is determined based on the maximisation of social welfare. Also, the congestion cost of a transmission system is defined as the difference between the cost of power supply before market clearing disregarding the transmission system (dispatching cost) and the operation cost of generating wind units of power suppliers considering transmission lines limit (re‐dispatching cost). Dispatching cost is calculated using economic load dispatch and the re‐dispatching cost is determined using optimal power flow. Moreover, the effect of wind units on transmission network reliability is studied through the relationship between wind units and lines loading and coherence between lines loading and lines failure rate. In addition, the effects of wind generation on composite transmission and generation reliability is formulated via the relationship between wind units and MCP and considering this price in loss of load and load shedding cost of customers. The proposed model is applied to the IEEE reliability test system and the results are discussed.
... Correa et al. (2014) presented an enhanced nonsorting genetic algorithm (NSGA)-II multiobjective algorithm for solving the security-constrained TEP problem. Seddighi and Ahmadi-Javid (2015) proposed a multistage stochastic programming model to address sustainable power generation and transmission expansion planning. Hemmati et al. (2016) dealt with a coordinated generation expansion planning (GEP)-TEP in the competitive electricity market in which uncertainties were simulated by Monte Carlo simulation. ...
This paper presents a model for a transmission expansion planning (TEP) problem in which both active and reactive power as well as voltage magnitude of buses are considered through linearized alternating current (AC) load-flow constraints. The proposed approach uses the special ordered set of Type 2 (SOS2) to obtain the optimal global solution of the approximated linear model of TEP, which is indeed a mixed-integer linear programming (MILP) problem. This linear binary model can be effectively solved by existing off-the-shelf solvers using the branch and bound algorithm. The solution obtained is guaranteed to be globally optimal, whereas most mixed-integer nonlinear programming (MINLP) solvers could not guarantee an obtainable global optimal solution for nonconvex problems. The accuracy level of the solutions for the approximated linearized model can be easily controlled by adjusting specific parameters to suitable values. Results obtained through a simulation study show the effectiveness and applicability of the linear model presented. As numerous simulation studies show, the proposed methodology is reliable and robust.
... Conventional research efforts have difficulties in addressing uncertainties and their interactions. Previously, a number of inexact optimization models were developed for handling uncertainties and their interactions (in water management and EPS planning problems) through fuzzy, stochastic, and interval programming approaches [21][22][23][24][25][26][27][28][29][30]. Among them, the interval fuzzy credibility-constrained programming (IFCP) method is a computationally efficient hybrid approach that can tackle epistemic uncertainties presented in the form of fuzzy membership functions and intervals. ...
In this study, an interval fuzzy-stochastic chance-constrained programming based energy-water nexus (IFSCP-WEN) model is developed for planning electric power system (EPS). The IFSCP-WEN model can tackle uncertainties expressed as possibility and probability distributions, as well as interval values. Different credibility (i.e., γ) levels and probability (i.e., qi) levels are set to reflect relationships among water supply, electricity generation, system cost, and constraint-violation risk. Results reveal that different γ and qi levels can lead to a changed system cost, imported electricity, electricity generation, and water supply. Results also disclose that the study EPS would tend to the transition from coal-dominated into clean energy-dominated. Gas-fired would be the main electric utility to supply electricity at the end of the planning horizon, occupying [28.47, 30.34]% (where 28.47% and 30.34% present the lower bound and the upper bound of interval value, respectively) of the total electricity generation. Correspondingly, water allocated to gas-fired would reach the highest, occupying [33.92, 34.72]% of total water supply. Surface water would be the main water source, accounting for more than [40.96, 43.44]% of the total water supply. The ratio of recycled water to total water supply would increase by about [11.37, 14.85]%. Results of the IFSCP-WEN model present its potential for sustainable EPS planning by co-optimizing energy and water resources.
... A multi-region optimisation model based on two-stage stochastic programming is formulated in [21] to find the optimal generation portfolios under demand uncertainty and different policy regimes. In [22], a generation and transmission planning is proposed using a multi-stage stochastic programming. The model incorporates uncertainties of electricity demand, fuel prices, greenhouse gas emissions as well as a number of sustainability regulations and policies. ...
This study deals with new challenges in the long-term planning of power systems in the presence of high variable renewable energy resources (VRE). To this end, a stochastic multi-stage planning model is proposed, through which the investment decisions on the new generation and transmission systems are made in several stages of the planning horizon considering the attributes of VRE resources. The model takes into account both long-term and short-term uncertainties through their plausible scenario realisations. The proposed model is formulated as a mixed-integer programming approach that cooptimises generation and transmission investments under uncertainty for alternative renewable policy targets. The model is applied in a realistic case of Queensland, Australia in which an equivalent network of the state is driven to accurately capture the existing and new generation candidates. The model results demonstrate that a 50% renewable energy target may increase the system costs - mostly through an increase in capital costs of cleaner technologies - by three to four times. The analysis also shows that the complementarity of solar photovoltaic and wind and their locations in the power system are important factors in deciding the optimal renewable-based investments.
... In Akbari and Tavakoli-Bina (2014), TEP is solved using the AC optimal power flow (AC-OPF) to provide accurate picture of power flow compared to the DC optimal power flow. Also, uncertainties about future electricity demand, fuel prices, greenhouse gas emissions, as well as possible disruptions can be incorporated in the planning models (Seddighi and Ahmadi-Javid 2015). Improved heuristic algorithms have been used for the solution of the power system planning. ...
This paper presents a market-based multi-period generation-transmission expansion planning (GTEP) along with fixed series compensation (FSC) allocation. FSCs can dispatch power more efficiently over the transmission network as well as trading opportunities for market participants and thus improve market surplus and reduce the total transmission investment. The proposed planning may accordingly enhance network efficiency and improve social welfare for all participants. The proposed model is structured as a mixed integer linear programming (MILP) problem. The CPLEX solver, as a commercial solver, is used to solve this MILP problem. Moreover, to find a reliable and viable optimal topology, N − 1 security criterion is employed through the proposed model. This criterion is used to take into account any unanticipated operating condition due to unexpected transmission line failures. The proposed model is applied to the Garver and IEEE 24-bus systems as well-known systems to show the effectiveness of FSC in dynamic GTEP.
This chapter is devoted to present a generalized model for scheduling multiple ambulance vehicles from multiple ambulance centers assigned to evacuate COVID-19 patients. The proposed formulation is a multi-objective multiple 0–1 mathematical model as a new application of the multi-objective multiple 0/1 knapsack problem.The scheduling aims at achieving the best utilization of the time shift as a planning time window. The best utilization of time is evaluated by a compromise between maximizing the number of evacuated people who might be infected with the virus to the isolation hospitals and maximizing the evacuated patients having higher relative priorities measured according to their health status. The complete mathematical model for the problem is formulated including the representation of binary decision variables, the problem constraints, and the multi-objective functions.The proposed multi-objective multiple ambulances model is applied to an illustrated case study in Great Cairo, Egypt, the case study aims at improving the scheduling of ambulance vehicles in the back-and-forth shuttle movements between patient locations and the available multiple isolation hospitals with multiple ambulance vehicles. The solution procedure is illustrated while two efficient solutions for the case study with different numbers of evacuated patients are obtained. The proposed mathematical model is so general that it can be applied to cases covering the whole Governorates and even the whole country all over the world.KeywordsScheduling shuttle ambulanceCOVID-19Quarantine casesBinary programmingMulti-objective multiple knapsack problem
The chapter develops a dynamic biobjective model for the generation expansion planning together with the transmission system expansion planning. A virtual database-supported nondominated sorting genetic algorithm, known as “VDS-NSGA-II,” is designed to tackle the multiyear multiobjective dynamic generation and transmission expansion planning (MMDGTEP) framework. The MMDGTEP is formulated as a biobjective optimization problem in this chapter, while the objective functions are defined as total cost minimization and also minimizing the expected energy not supplied (EENS) at the hierarchy level II, known as EENSHL-II. The first objective function is comprised of the investment and operating costs. The proposed hybrid model is decomposed into two programming problems: master problem and slave problem. In the first level, that is the master level, a virtual mapping procedure (VMP) is incorporated in the VDS-NSGA-II to evaluate the contrast of each capacity additions in the planning horizon. In the second level, that is the slave problem, a linear programming approach is employed to assess the objectives of the problem. The virtual database helps reduce the computational burden. By avoiding the monotonous calculation in the proposed framework, the convergence time is reduced, appropriately. After obtaining the optimal Pareto set, the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) decision maker is used to pick the most desired Pareto-optimal solution. The presented long-term planning model is simulated on a test power system to verify the effectiveness and efficiency of the framework.
This paper proposes a stochastic programming model and a combined solution algorithm to solve integrated resource planning (IRP) problem of electric power systems in which supply and demand side resources are combined to construct a pool of resources to expand the power systems. The problem is formulated as a two-stage recourse model, where random uncertainties in demand, operating costs, equivalent availability of generation units and customer responses to demand side management programs are taken into account. The solution methodology integrates an exterior sampling strategy, the sample average approximation algorithm, with an accelerated Benders decomposition algorithm to compute high quality solutions to the stochastic IRP problem with exponentially large number of scenarios. The proposed integrated algorithm is implemented on the modified 6, 21 and 48 bus IEEE reliability test systems and the confidence intervals of lower and upper bounds of optimal objective function as well as optimality gap are reported.
China is transiting its power system towards a more flexible status with a higher capability of integrating renewable energy generation. Demand response (DR) and energy storage increasingly play important roles to improve power system flexibility. The coordinated development of power sources, network, DR, and energy storage will become a trend. This paper examines the significance of source‐network‐demand‐storage coordinated development. Furthermore, an outlook of the power system transition in China is provided by virtue of source‐network‐demand‐storage coordinated planning. The paper also assesses the integration of multiple urban infrastructures in China and its impacts on source‐network‐demand‐storage coordination. Lastly, challenges for achieving the source‐network‐demand‐storage coordination and suggested measures are discussed.
This article is categorized under: Energy Research & Innovation > Systems and Infrastructure
Energy and Urban Design > Systems and Infrastructure
Indonesia has targets on renewable energy (RE) development as shown in National Energy Policy. The RE plan should be carefully planned because it is so important. Uncertainty is a big problem in RE development. To handle correctly the uncertainty, it needs to understand all aspects about the uncertainty. A comprehensive review is needed to get a complete understanding of the uncertainty. After that, it can be known the prospects of RE implementation to reach the targets. This research has done a comprehensive review on uncertainty problems in power systems and has done an analysis about the prospects of Indonesia's RE to fulfil their targets. The research's aims are to find a method to handle correctly the uncertainty and to know the RE prospect to fulfil the targets. Review's results showed the appropriate generation expansion planning model to handle uncertainties is independent-Montecarlo simulation-Brownian motion by considering uncertainty on demand side, power plant, and transmission & distribution (T&D), energy resource, and government policy. The RE resources, the current situation of RE usage, the RE target, and the existing of government planning on RE power plants development showed RE has high prospects and capable to fulfil their target.
Electricity infrastructure confronts societies with immense costs as it must ensure the generation of power and its transmission to locations with consumption requirements. We minimize these costs by formulating an electricity generation and transmission problem that facilitates the design of electricity infrastructure on a macro level. Our problem specifies the capacity, type, and location of power plants and, at the same time, determines the appropriate arrangement of high-voltage transmission lines in order to fulfill the demand of individual cities. We specifically incorporate the non-linear nature of cost functions for power generation that are common in practice. This results in a mixed integer non-linear problem, for which the branch-and-reduce solver from GAMS exceeds runtime constraints, even for small instances with 25 locations. As a remedy, we develop heuristics based on the reduced variable neighborhood search and the greedy randomized adaptive search procedure (GRASP). Their performance enables us to address large-scale problems that arise in real-world applications. We demonstrate this with an actual, nationwide example that spans all 4537 municipalities in Germany.
in this paper we will show how wind farms affect local marginal prices (LMPs), rates of line failure, reliability of transmission and generation, and operation costs for TNEP in the market.
In order to model the transmission reliability, load shedding (LS) criterion LMP and outage durations were used in a single line failure or concurrent failure of wind and line units. Outage duration is a period that a line fails and a time required to reestablish the failed line. In generation reliability formulation, loss of load (LOL) and LS criteria were utilized as a consequence of wind unit outage, generation and congestion in transmission other than the value of lost load (VOLL), LMP and outage periods (failure and repair periods of units). In addition, the relation between wind unit and line loading was used to formulate the effects of wind units on LMP and line failure rates. Operation expense of generation system that includes pollution cost, fuel cost, and generation charge of energy by wind units is calculated using optimal power flow (OPF). Moreover, the status of network loss is influenced by the wind units is modeled via the bonding between line loading and wind units. The suggested model was used to evaluate the IEEE Reliability Test System (IEEE RTS).
Finally, the results will be discussed.
The economic incentive to transmission investment considering environmental cost is investigated in this paper. The definition of economically stimulating signals to transmission investment is given. Based on the transmission line optimal investment planning model proposed in Brazil, the transmission planning model with economic stimulation is proposed. What’s more, the external cost of environment is taken into account in this above transmission planning model. This model is suitable for the merchant transmission plan. And it also provides transmission investors a decision-making reference. The effectiveness of the economic stimulation to transmission investment considering environmental cost is verified by the 5-bus system.
Power system planning for modern distribution networks is undergoing a change because of distributed power generation and grid ancillary services. The vast electricity retail utility industry with many distribution network operators plans generation and transmission expansion planning for determining optimal investment decisions. This paper addresses this planning problem in a decentralized and distributed context.
This paper introduces a coordinated decision making approach for optimal investments in generation and transmission expansion planning problems for distribution networks. The distribution networks are further classified into microgrids. Considering an agent as the functional information exchanging entity just like an energy meter with the objective to coordinate the expansion decisions of participants; a novel math-heuristic optimization model Coordinated Microgrid is presented. To simulate the coordination of information a multi-agent-system based coordinated decision making method is adopted and the value of coordination is investigated. The evolutionary vertical sequencing protocol, a heuristic method, is developed and implemented to simulate the coordination process among agents on the top level. The proposed protocol produces smart permutations of microgrids for coordination. On the bottom level, a two-stage chance-constrained stochastic MILP formulation for investment decisions with operational uncertainties is modeled. For market clearing a nodal-pricing scheme is adopted that maintains the Nash equilibrium among and across the microgrids for energy transactions. The proposed model is tested with consumption, network configuration data from three islands in west-coast of Norway. The models are solved to optimality and results lead to the observations that the value of coordination lies in profit increment of individual microgrid. The novel protocol proposed demonstrates an advantage of retrieving smart permutations from combinations of microgrids. In summary, CoMG is a novel expansion planning model for optimal investments in modern power distribution networks.
While methods for optimization under uncertainty have been studied intensely over the past decades, the explicit consideration of the interplay between uncertainty and time has gained increasing attention rather recently. Problems requiring a sequence of decisions in reaction to uncertainty realizations are of crucial relevance in real-world applications, e.g., supply chain planning, scheduling, or finance. Several methods emphasizing varying aspects of these problems have been developed, mainly triggered by a particular application. Although these methods all intend to solve a similar underlying problem, they differ strongly with respect to the uncertainty representation, the prescriptive solution information they provide and the means of performance evaluation. The result is a fragmented picture of uncertain multi-stage problems – both from a methodological and an application-oriented perspective. It fails to interconnect results from different disciplines or even comparing strengths and weaknesses of individual methods in particular applications. This review aims at integrating the different methods for solving uncertainty inflicted multi-stage optimization problems into a broader picture, thereby paving the way for more comprehensive approaches to sequential decision making under uncertainty. For this purpose, a description of the methods along with their historic development is given first. Secondly, an overview on their main areas of application is provided. We conclude that decoupling uncertainty models from solution methods and developing standardized performance measures represent key steps for organizing multi-stage optimization under uncertainty and for eliciting further potentials of yet unexplored combinations of uncertainty models and solution methods.
Growing demand for electricity has made power grid design and expansion planning one of the main challenges in power industry management. In recent years, reconfiguration of existing power grid along with the adoption of renewable power generation leads to a significant reduction in expansion costs and GHG emissions. This paper offered a novel optimization model to address the design and planning of power grid expansion in a dynamic environment. Besides capacity planning, the model also determines location and time to construct new facilities. This research aims to satisfy demand by considering reduction in net present value of costs and increase in network efficiency. Electricity tariff and cost of load shedding differ according to different power consumers (i.e., residential, commercial, industrial and agricultural). Due to inherent intermittency in renewable energy resources and their subsequent impact on the entire power grid, different scenarios are generated, and the model is solved using the sample average approximation method. Eventually, validation of the proposed model and sensitivity analysis is carried out through a real case study in Iran. Computational results demonstrate the practicality of the stochastic model and show integration of renewable power plants would decrease the transmission and sub-transmission network costs.
Generation expansion planning (GEP) is a power plant mix problem that identifies what, where, when, and how new generating facilities should be installed and when old units be retired over a specific planning horizon. GEP ensures that the quantity of electricity generated matches the electricity demand throughout the planning horizon. This kind of planning is of importance because most production and service delivery is dependent on availability of electricity. Over the years, the traditional GEP approaches have evolved to produce more realistic models and new solution algorithms. For example, with the agitation for green environment, the inclusion of renewable energy plants and energy storage in the traditional GEP model is gradually gaining attention. In this regards, a handful of research has been conducted to identify the optimal expansion plans based on various energy‐related perspectives. The appraisal and classification of studies under these topics are necessary to provide insights for further works in GEP studies. This article therefore presents a comprehensive up‐to‐date review of GEP studies. Result from the survey shows that the integration of demand side management, energy storage systems (ESSs), and short‐term operational characteristics of power plants in GEP models can significantly improve flexibility of power system networks and cause a change in energy production and the optimal capacity mix. Furthermore, this article was able to identify that to effectively integrate ESS into the generation expansion plan, a high temporal resolution dimension is essential. It also provides a policy discussion with regard to the implementation of GEP. This survey provides a broad background to explore new research areas in order to improve the presently available GEP models.
This work analyzes the impacts on the power system expansion planning of implementing CO 2 and local pollutant emission taxes under five different policy-relevant scenarios. To do this, we have formulated and implemented an optimization model based on a mixed-integer linear program, which determines the optimal expansion plan considering the installation of both large-scale power plants and renewable-based distributed generation. An important characteristic of the proposed model is that it includes a detailed formulation of the power system. Moreover, differently than existing literature, special attention is given to the analysis of the spatial-temporal distributive effects of pollutant taxes, considering both global and local pollutant emissions. The method is applied to the main Chilean power system. Our results indicate that global and local pollutant taxes significantly impact both planning and operational decisions in the power system. In particular, pollutant taxes may have significant spatial distributive effects, as shown in the analysis of 13 regions of Chile, leading to damages in some specific regions while relatively benefiting others. Our results also show that the availability of renewable energy capacity may improve the effectiveness of pollutant taxes. Particularly, adding 1.5 GW of hydro capacity to the Chilean system allows avoiding around 32 GWh of fossil fuel generation per year, saving more than 1.5 billion US$ in the 10-year horizon considered. The proposed method and qualitative results are sufficiently generic to apply to any other jurisdiction.
This paper studies transmission expansion planning (TEP) problem considering effects of wind farms on transmission network congestion, power system reliability, line repair, and operational costs of wind and generating units in a deregulated environment. Transmission congestion is modeled using difference between local marginal prices (LMPs) of start and end buses of each line that is multiplied by its power flow. In this way, wind units can efficiently affect network congestion through decrease in lines loading and LMPs of buses. Transmission system reliability is modeled using load shedding (LS) index as well as outage rate and outage duration of a single line or simultaneous outage of a line and a wind unit. Also, generation reliability is formulated using loss of load, value of lost load, and LS criteria due to outage rates and outage durations of generating and wind units considering line limits. In addition, the effect of wind units on repair cost of transmission lines is modeled by the relationship among wind generation, line failure rates and lines loading. Operational cost includes cost of network losses, pollution penalty, fuel expenses, and generation cost of wind units. In order to more efficiently investigate the effect of wind power on TEP problem, the proposed model was tested on the IEEE Reliability Test System (IEEE RTS) for different scenarios, and the results were discussed.
A challenging aspect of applying stochastic programming in a dynamic setting is to construct a set of discrete scenarios that well represents multivariate stochastic processes for uncertain parameters. Often this is done by generating a scenario tree using a statistical procedure and then reducing its size while maintaining its statistical properties. In this paper, we test a new scenario reduction heuristic in the context of long-term power generation expansion planning. We generate two different sets of scenarios for future electricity demands and fuel prices by statistical extrapolation of long-term historical trends. The cardinality of the first set is controlled by employing increasing length time periods in a tree structure while that of the second set is limited by its lattice structure with periods of equal length. Nevertheless, some method of scenario thinning is necessary to achieve manageable solution times. To mitigate the computational complexity of the widely-used forward selection heuristic for scenario reduction, we customize a new heuristic scenario reduction method named forward selection in wait-and-see clusters (FSWC) for this application. In this method, we first cluster the scenarios based on their wait-and-see solutions and then apply fast forward selection within clusters. Numerical results for a twenty year generation expansion planning case study indicate substantial computational savings to achieve similar solutions as those obtained by forward selection alone.
The geometric Brownian motion (GBM) process is frequently invoked as a model for such diverse quantities as stock prices, natural resource prices and the growth in demand for products or services. We discuss a process for checking whether a given time series follows the GBM process. Methods to remove seasonal variation from such a time series are also analyzed. Of four industries studied, the historical time series for usage of established services meet the criteria for a GBM; however, the data for growth of emergent services do not.
We discuss the energy generation expansion planning with environmental constraints, formulated as a nonsmooth convex constrained optimization problem. To solve such problems, methods suitable for constrained nonsmooth optimization need to be employed. We describe a recently developed approach, which applies the usual unconstrained bundle techniques to a dynamically changing "improvement function". Numerical results for the generation expansion planning are reported.
We present an applied mathematical model with stochastic input data for mean-risk optimization of electricity portfolios containing electricity futures as well as several components to satisfy a stochastic electricity demand: electricity spot market, two different types of supply contracts offered by a large power producer, and a combined heat and power production facility with limited capacity. Stochasticity enters the model via uncertain electricity demand, heat demand, spot prices, and future prices. The model is set up as a decision support system for a municipal power utility (price taker) and considers a medium term optimization horizon of one year in hourly discretization. The objective is to maximize the expected overall revenue and, simultaneously, to minimize risk in terms of multiperiod risk measures. Such risk measures take into account intermediate cash values in order to avoid uncertainty and liquidity problems at any time. We compare the effect of different multiperiod risk measures taken from the class of polyhedral risk measures which was suggested in our earlier work.
Climate change poses a huge threat to human welfare. Hence, developing a low-carbon economy has become a prevailing and inevitable trend. Decarbonization of power generation, especially converting the current power mix into a low-carbon structure, will be a critical option for CO2 emission mitigation. In this paper, an integrated power generation expansion (PGE) planning model towards low-carbon economy is proposed, which properly integrates and formulates the impacts of various low-carbon factors on PGE models. In order to adapt to the characteristics of PGE models based on low-carbon scenario, a compromised modeling approach is presented, which reasonably decreases complexities of the model, while properly keeping the significant elements and maintaining moderate precision degree. In order to illustrate the proposed model and approach, a numerical case is studied based on the background of China's power sector, making decisions on the optimal PGE plans and revealing the prospects and potentials for CO2 emission reduction.
Power generation expansion planning inherently involves multiple, conflicting and incommensurate objectives. Therefore, mathematical models become more realistic if distinct evaluation aspects, such as cost and environmental concerns, are explicitly considered as objective functions rather than being encompassed by a single economic indicator. With the aid of multiple objective models, decision makers may grasp the conflicting nature and the trade-offs among the different objectives in order to select satisfactory compromise solutions. This paper presents a multiple objective mixed integer linear programming model for power generation expansion planning that allows the consideration of modular expansion capacity values of supply-side options. This characteristic of the model avoids the well-known problem associated with continuous capacity values that usually have to be discretized in a post-processing phase without feedback on the nature and importance of the changes in the attributes of the obtained solutions. Demand-side management (DSM) is also considered an option in the planning process, assuming there is a sufficiently large portion of the market under franchise conditions. As DSM full costs are accounted in the model, including lost revenues, it is possible to perform an evaluation of the rate impact in order to further inform the decision process.
The development of the new seismic hazard map of Iran is based on probabilistic seismic hazard computation using the historical earthquakes data, geology, tectonics, fault activity and seismic source models in Iran. These maps have been prepared to indicate the earthquake hazard of Iran in the form of iso-acceleration contour lines, and seismic hazard zoning, by using current probabilistic procedures. They display the probabilistic estimates of Peak Ground Acceleration (PGA) for the return periods of 75 and 475 years. The maps have been divided into intervals of 0.25 degrees in both latitudinal and longitudinal directions to calculate the peak ground acceleration values at each grid point and draw the seismic hazard curves. The results presented in this study will provide the basis for the preparation of seismic risk maps, the estimation of earthquake insurance premiums, and the preliminary site evaluation of critical facilities.
Exposure to noise constitutes a health risk. There is sufficient scientific evidence that noise exposure can induce hearing impairment, hypertension and ischemic heart disease, annoyance, sleep disturbance, and decreased school performance. For other effects such as changes in the immune system and birth defects, the evidence is limited. Most public health impacts of noise were already identified in the 1960s and noise abatement is less of a scientific but primarily a policy problem. A subject for further research is the elucidation of the mechanisms underlying noise-induced cardiovascular disorders and the relationship of noise with annoyance and nonacoustical factors modifying health outcomes. A high priority study subject is the effects of noise on children, including cognitive effects and their reversibility. Noise exposure is on the increase, especially in the general living environment, both in industrialized nations and in developing world regions. This implies that in the twenty-first century noise exposure will still be a major public health problem.
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The power generation expansion planning (PGEP) problem is a large-scale mixed integer nonlinear programming (MINLP) problem cited as one of the most complex optimization problems. In this paper, an application of a new efficient methodology for solving the power generation expansion planning problem is presented. A comprehensive planning production simulation model is introduced toward formulating into an MINLP model. The model evaluates the most economical investment planning for additional thermal power generating units of the optimal mix for long-term power generation expansion planning with emission controls, regarding to the incorporated environmental costs, subject to the integrated requirements of power demands, power capacities, loss of load probability (LOLP) levels, locations, and environmental limitations for emission controls. A GA-heuristic-based method called GA-Benders' decomposition (GA-BD) is proposed for solving this complex problem. Finally, an application of the proposed GA-BD method is discussed and concluded.
This paper presents a mixed-integer LP approach to the solution of the long-term transmission expansion planning problem. In general, this problem is large-scale, mixed-integer, nonlinear, and nonconvex. We derive a mixed-integer linear formulation that considers losses and guarantees convergence to optimality using existing optimization software. The proposed model is applied to Garver's 6-bus system, the IEEE Reliability Test System, and a realistic Brazilian system. Simulation results show the accuracy as well as the efficiency of the proposed solution technique.
The generation expansion-planning problem (GEP) is a large-scale stochastic nonlinear optimization problem. To handle the problem complexity, decomposition schemes have been used. Usually, such schemes divide the expansion problem into two subproblems: one related to the construction of new plants (investment subproblem) and another dealing with the task of operating the system (operation subproblem). This paper proposes an iterative genetic algorithm (IGA) to solve the investment subproblem. The basic idea is to use a special type of chromosome, christened pointer-based chromosome (PBC), and the particular structure of that subproblem, to transform an integer constrained problem into an unconstrained one. IGA's results were compared to those of a branch and bound (B&B) algorithm-provided by a commercial package-in three different case studies of growing complexity, respectively, containing 144, 462, and 1845 decision variables. These results indicate that the IGA is an effective alternative to the solution of the investment subproblem.
The classical nonlinear mixed integer formulation of the
transmission network expansion problem cannot guarantee finding the
optimal solution due to its nonconvex nature. We propose an alternative
mixed integer linear disjunctive formulation, which has better
conditioning properties than the standard disjunctive model. The mixed
integer program is solved by a commercial branch and bound code, where
an upper bound provided by a heuristic solution is used to reduce the
tree search. The heuristic solution is obtained using a GRASP
metaheuristic, capable of finding sub-optimal solutions with an
affordable computing effort. Combining the upper bound given by the
heuristic and the mixed integer disjunctive model, optimality can be
proven for several hard problem instances
Third edition. "Since publication of the second edition, there have been extensive changes in the algorithms, methods, and assumptions in energy management systems that analyze and control power generation. This edition is updated to acquaint electrical engineering students and professionals with current power generation systems. Algorithms and methods for solving integrated economic, network, and generating system analysis are provided. Also included are the state-of-the-art topics undergoing evolutionary change, including market simulation, multiple market analysis, multiple interchange contract analysis, contract and market bidding, and asset valuation under various portfolio combinations"-- "Online video course with powerpoint slides for each chapter at www.cusp.umn.edu; site also contains links to important research reports, an entire set of student programs in MATLAB, and sets of power system sample data sets for use in student exercises"-- Preface to the third edition -- Preface to the second edition -- Preface to the first edition -- Acknowledgment -- Introduction -- Industrial organization, managerial economics, and finance -- Economic dispatch of thermal units and methods of solution -- Unit commitment -- Generation with limited energy supply --Transmission system effects -- Power system security -- Optimal power flow -- Introduction to state estimation in power systems -- Control of generation -- Interchange, pooling, brokers, and auctions -- Short-term demand forecasting -- Index.
This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject.
The ocean has absorbed a significant portion of all human-made carbon dioxide emissions. This benefits human society by moderating the rate of climate change, but also causes unprecedented changes to ocean chemistry. Carbon dioxide taken up by the ocean decreases the pH of the water and leads to a suite of chemical changes collectively known as ocean acidification. The long term consequences of ocean acidification are not known, but are expected to result in changes to many ecosystems and the services they provide to society. Ocean Acidification: A National Strategy to Meet the Challenges of a Changing Ocean reviews the current state of knowledge, explores gaps in understanding, and identifies several key findings. Like climate change, ocean acidification is a growing global problem that will intensify with continued CO2 emissions and has the potential to change marine ecosystems and affect benefits to society. The federal government has taken positive initial steps by developing a national ocean acidification program, but more information is needed to fully understand and address the threat that ocean acidification may pose to marine ecosystems and the services they provide. In addition, a global observation network of chemical and biological sensors is needed to monitor changes in ocean conditions attributable to acidification. © 2010 by the National Academy of Sciences. All rights reserved.
In risk management it is desirable to grasp the essential statistical features of a time series representing a risk factor. This paper aims to introduce a number of different stochastic processes that can help in grasping the essential features of risk factors, describing different asset classes or behaviours. The paper does not aim to be exhaustive, but gives examples and a feeling for practically implementable models, allowing for stylised features in the data. These models can also be used as building blocks to build more complex models, although, for a number of risk management applications, the models developed here suffice for the first step in the quantitative analysis. The broad qualitative features addressed here are fat tails. In the second part of this work to appear in a subsequent paper, mean reversion is addressed with and without fat tails. The paper gives some orientation on the initial choice of a suitable stochastic process and then explains how the process parameters can be estimated based on historical data. Once the process has been calibrated, typically through maximum likelihood estimation, one may simulate the risk factor and build future scenarios for the risky portfolio. On the terminal simulated distribution of the portfolio, one may then single out several risk measures, although the present paper focuses on the stochastic processes estimation preceding the simulation of the risk factors. Finally, this paper focuses on single time series. Correlation or more generally dependence across risk factors, leading to multivariate processes modelling, will be addressed in future work.
Power expansion planning is one of the key challenges in power systems management, both in theory and application. Power generation significantly affects society and the environment in several ways, such as greenhouse gas emissions, air and noise pollution, hazardous waste, life-threatening issues and social expectations. These factors become more important in power systems facing hazards and disasters that may result in system disruptions. This paper considers generation expansion planning using a sustainable risk-averse approach that addresses both the socio-environmental factors and the disruption risks involved in a power system. The approach uses a mathematical formulation that can be linearized as a mixed-integer linear programming model. The approach is applied to the case of Iran’s southwestern power grid. The computational results demonstrate the importance of incorporating disruption risks and sustainability issues into power systems planning.
Our key research objective in this study is to examine whether investments in corporate social responsibility (CSR) have an effect on corporate financial performance (CFP), or vice versa. The context is the energy industry, in which sustainability issues are of vital importance. Our data set is compiled from the KLD database and Thomson ONE. We use panel data on energy-sector companies covering the years 1991 and 2009 in order to assess Granger causality between CSR strengths/concerns and CFP. We consider strengths and concerns separately, and use both accounting and market-based measures of CFP. Our findings indicate differing impacts on financial performance: CSR concerns Granger-cause both profitability and market value whereas CSR strengths seem only to Granger-cause market value. These effects appear after different delays. Furthermore, as CFP does not seem to Granger-cause CSP in most of the model specifications, our results do not support bidirectional causality between CSP and CFP.
This study presents a performance comparison of metaheuristics to solve transmission expansion planning (TEP) problems in power systems. The proposed methodology includes the search for the least cost solution, bearing in mind investments and operational costs related to ohmic transmission losses. The multi-stage nature of the TEP is also taken into consideration. Case studies on a small system and on a real sub-transmission network are presented and discussed.
We present a three-level equilibrium model for the expansion of an electric network. The lower-level model represents the equilibrium of a pool-based market; the intermediate level represents the Nash equilibrium in generation capacity expansion, taking into account the outcomes on the spot market; and the upper-level model represents the anticipation of transmission expansion planning to the investment in generation capacity and the pool-based market equilibrium. The demand has been considered as exogenous and locational marginal prices are obtained as endogenous variables of the model. The three-level model is formulated as a mixed integer linear programming (MILP) problem. The model is applied to a realistic power system in Chile to illustrate the methodology and proper conclusions are reached.
This study investigates the role of public relations in managing social responsibility in a group of Colombian electricity sector companies. According to the results, communication professionals who support social responsibility programs do not hold a unified concept of public relations. Furthermore, not all of them acknowledge the currently prevailing model, which regards the practice of public relations as seeking both harmony with its environment and mutual benefit with the public interest.
After the deregulation of the Power Sector in the U.S., planning for generation and transmission capacities is decentralized. There is, however, still need for the long term integrated planning of generation and transmission capacities at the macro level, since these two sectors must operate in a coordinated manner. This paper presents a model for integrating generation and transmission expansion planning to identify an indicative expansion plan for the total sector at the macro level. The argument for an integrated model is supported using evidence from integrated planning efforts in real life systems. The application of the proposed model is illustrated using an example that requires expansion of generation and transmission capacities over three regions in a deregulated power system. The example considers that addition of generating capacity should come from renewable sources. The test results show the potential cost saving from integrated planning.
Generation expansion planning is defined as the problem of finding the technology type, number of generation units, size, and location of candidate plants within the planning horizon. In the deregulated environment rather than the traditional system which considered the cost minimization as the main objective function in generation expansion planning problem, the major objective is to maximize the Project Lifetime Economic Return. In this paper, the problem is solved considering three objectives, simultaneously (i.e. maximization of the Project Lifetime Economic Return, minimization of CO2 emission, and minimization of the fuel price risk due to the use of non-renewable energy sources). Furthermore, due to the extensive use of renewable energy sources, e.g., onshore wind, offshore wind, solar, etc, the effect of these power plants has been investigated in this paper. In order to make the problem more compatible with the real world, some of the most common incentive systems (i.e. carbon tax, emission trade, quota obligation, and feed-in-tariff) have been considered for the problem formulation. The problem is solved using Modified Normal Boundary Intersection method using General Algebraic Modelling System. Finally, a case study is designed to assess the efficiency of the proposed scheme.
The explicit incorporation of uncertainty in transmission network design can help to improve the balance between different and important concerns such as network utilization, demand satisfaction, or dynamic sourcing from lower-cost generation options. The explicit study of mean-risk trade-offs in network design can also better support planners in risk-related decisions. With these motivations, we present in this paper a mean-risk mixed integer linear programming model for transmission network expansion planning. The model has the potential to be used in practical applications, but in the scope of the paper is used to search for network design insights, with a study of loss-averse design of three fundamental network building blocks – an independent design, a radial design, and a meshed design. The study illustrates how different network designs feature different trade-offs between mean cost minimization and risk mitigation, focusing on the impact of network structure, loss aversion, variability, and demand correlation.
This paper presents a comprehensive optimal expansion planning model for an integrated generation and transmission system. The objective function used in the optimization model comprises of the capital cost of the new generating units to be built, the fuel cost incurred in running all the generating units in the system including the transportation cost of fuel from the fuel source ends to the generating unit locations and the capital cost of the new transmission lines to be installed for meeting the forecasted system demand at the target planning year. Constraints taken care of in the model include the fuel availability limits at the fuel sources, the fuel transportation limits for the transportation of fuels from fuel sources to the generating unit locations, capacity of generating units required to be built as well as the power transmission limits of the transmission lines in the system. The developed model is tested on a system to bring out the relative advantage of adopting the integrated generation and transmission expansion planning approach as compared to the sequential approach of first planning the generation expansion and then the transmission expansion. The model has also been applied to the integrated generation and transmission expansion planning of a real system.
The awareness of the impact of human activities in society and environment is known as "Social Responsibility" (SR). It has been a topic of growing interest in many enterprises since the fifties of the past Century, and its implementation/assessment is nowadays supported by international standards. There is a tendency to amplify its scope of application to other areas of the human activities, such as Research, Development and Innovation (R+D+I). In this paper, a model of quantitative assessment of Social Responsibility in Environmental Science and Technology (SR EST) is described in detail. This model is based on well established written standards as the EFQM Excellence model and the ISO 26000:2010 Guidance on SR. The definition of five hierarchies of indicators, the transformation of qualitative information into quantitative data and the dual procedure of self-evaluation and external evaluation are the milestones of the proposed model, which can be applied to Environmental Research Centres and institutions. In addition, a simplified model that facilitates its implementation is presented in the article.
This paper describes a methodology to incorporate the environmental costs associated to the construction and operation of power plants in the long-term expansion planning process of hydrothermal generation systems. These external costs are estimated in terms of monetary values, according to the nature of their impacts and endogenously included in the formulation of the expansion planning model. The minimization of the maximum regret framework used in the modeling process enables the development of a single expansion strategy that allows for corrections in the expansion trajectory, according to the behavior of electricity demand. A case study based on the Brazilian system and previous environmental valuation studies is presented and discussed. The results found contemplate a reduction in the total cost of the electricity system expansion planning.
This paper presents a model to solve the Generation Expansion Planning (GEP), problem in competitive electricity markets. The developed approach recognizes the presence of several generation agents aiming at maximizing their profits and that the planning environment is influenced by uncertainties affecting the demand, fuel prices, investment and maintenance costs and the electricity price. Several of these variables have interrelations between them turning it important to develop an approach that adequately captures the long-run behavior of electricity markets. In the developed approach we used System Dynamics to capture this behavior and to characterize the evolution of electricity prices and of the demand. Using this information, generation agents can then prepare their individual expansion plans. The resulting individual optimization problems have a mixed integer nature, justifying the use of Genetic Algorithms (GAs). Once individual plans are obtained, they are input once again on the System Dynamics model to update the evolution of the price, of the demand and of the capacity factors. This defines a feedback mechanism between the individual expansion planning problems and the long-term System Dynamics model. This approach can be used by a generation agent to build a robust expansion plan in the sense it can simulate different reactions of the other competitors and also by regulatory or state agencies to investigate the impact of regulatory decisions on the evolution of the generation system. Finally, the paper includes a Case Study to illustrate the use and the results of this approach.
In this paper, an adaptive simulated annealing genetic algorithm is proposed to solve generation expansion planning of Turkey's power system. Least-cost planning is a challenging optimization problem due to its large-scale, long-term, nonlinear, and discrete nature of power generation unit size. Genetic algorithms have been successfully applied during the past decade, but they show some limitations in large-scale problems. In this study, simulated annealing is used instead of mutation operator to improve the genetic algorithm. The improved algorithm is applied to the power generation system with seven types of generating units and a 20-year planning horizon. The planning horizon is divided into four equal periods. The new algorithm provides approximately 6.6 billion US$ (3.2%) cheaper solution than GA and also shows faster convergence. Copyright © 2006 John Wiley & Sons, Ltd.
We formulate a generation expansion planning problem to determine the type and quantity of power plants to be constructed
over each year of an extended planning horizon, considering uncertainty regarding future demand and fuel prices. Our model
is expressed as a two-stage stochastic mixed-integer program, which we use to compute solutions independently minimizing the
expected cost and the Conditional Value-at-Risk; i.e., the risk of significantly larger-than-expected operational costs. We
introduce stochastic process models to capture demand and fuel price uncertainty, which are in turn used to generate trees
that accurately represent the uncertainty space. Using a realistic problem instance based on the Midwest US, we explore two
fundamental, unexplored issues that arise when solving any stochastic generation expansion model. First, we introduce and
discuss the use of an algorithm for computing confidence intervals on obtained solution costs, to account for the fact that
a finite sample of scenarios was used to obtain a particular solution. Second, we analyze the nature of solutions obtained
under different parameterizations of this method, to assess whether the recommended solutions themselves are invariant to
changes in costs. The issues are critical for decision makers who seek truly robust recommendations for generation expansion
planning.
KeywordsGeneration expansion planning–Stochastic programming–Scenario generation–Multiple replication procedure–Solution stability
This paper describes use of a multiobjective optimization method, elitist nondominated sorting genetic algorithm version II (NSGA-II), to the generation expansion planning (GEP) problem. The proposed model provides for decision maker choice from among the different trade-off solutions. Two different problem formulations are considered. In one formulation, the first objective is to minimize cost; the second objective is to minimize sum of normalized constraint violations. In the other formulation, the first objective is to minimize investment cost; the second objective is to minimize outage cost (or maximize reliability). Virtual mapping procedure is introduced to improve the performance of NSGA-II. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.
Generation expansion planning (GEP) is the problem of finding the optimal strategy to plan the construction of new generation plants while satisfying technical and economical constraints. It is a challenging problem due to its nonlinearity, large-scale, and to the discrete nature of the variables describing unit size and allocation. Originally, GEP was faced by vertically integrated utilities with the aim of minimizing production and capital costs. After deregulation, generation companies were forced to consider GEP from the viewpoint of market shares and financial risk. In recent years, increasing concern for environmental protection has driven lots of countries all over the world to promote energy generation from renewable sources. Different incentive systems have been introduced to support the growth of the investments in generation plants exploiting renewable energy. In the present paper, the impact of some of the most popular incentive systems (namely feed-in tariffs, quota obligation, emission trade, and carbon tax) on generation planning is considered, thus obtaining a comprehensive GEP model with a suitably modified objective function and additional constraints. The resulting problem is solved by resorting to the generalized Benders decomposition (GBD) approach and implemented in the Matlab programming language. Tests are presented with reference to the Italian system.
A new approach to the electricity generation expansion problem is proposed to minimize simultaneously multiple objectives, such as cost and air emissions, including CO2 and NOx, over a long term planning horizon. In this problem, system expansion decisions are made to select the type of power generation, such as coal, nuclear, wind, etc., where the new generation asset should be located, and at which time period expansion should take place. We are able to find a Pareto front for the multi-objective generation expansion planning problem that explicitly considers availability of the system components over the planning horizon and operational dispatching decisions. Monte-Carlo simulation is used to generate numerous scenarios based on the component availabilities and anticipated demand for energy. The problem is then formulated as a mixed integer linear program, and optimal solutions are found based on the simulated scenarios with a combined objective function considering the multiple problem objectives. The different objectives are combined using dimensionless weights and a Pareto front can be determined by varying these weights. The mathematical model is demonstrated on an example problem with interesting results indicating how expansion decisions vary depending on whether minimizing cost or minimizing greenhouse gas emissions or pollutants is given higher priority.
After deregulation of the Power sector, uncertainty has increased considerably. Vertically integrated utilities were unbundled into independent generation, transmission and distribution companies. Transmission network expansion planning (TNEP) is now performed independent from generation planning. In this environment TNEP must include uncertainties of the generation sector as well as its own. Uncertainty in generation costs affecting optimal dispatch and uncertainty in demand loads are captured through composite scenarios. Probabilities are assigned to different scenarios. The effects of these uncertainties are transferred to the objective function in terms of total costs, which include: generation (dispatch), transmission expansion and load curtailment costs. Two formulations are presented: stochastic and minimum regret. The stochastic formulation seeks a design with minimum expected cost. The minimum regret formulation seeks a design with robust performance in terms of variance of the operational costs. Results for a test problem and a potential application to a real system are presented.Journal of the Operational Research Society (2008) 59, 1547–1556. doi:10.1057/palgrave.jors.2602492 Published online 12 September 2007
A novel constructive heuristic algorithm to the network expansion planning problem is presented. The basic idea comes from Garver's work applied to the transportation model, nevertheless the proposed algorithm is for the DC model. Tests results with most known systems in the literature are carried out to show the efficiency of the method.
A long-term multiobjective model for the power generation expansion planning of electric systems is described and evaluated in this paper. The model optimizes simultaneously multiple objectives (i.e., minimizes costs, environmental impact, imported fuel and fuel price risks) and decides the location of the planned generation units in a multiperiod planning horizon. Among the attributes considered in the model are the investment and operation cost of the units, the environmental impact, the amount of imported fuel, and the portfolio investment risk. The approach to solve this problem is based on multiobjective linear programming and the analytical hierarchy process. A case study from the Mexican Electric Power System is used to illustrate the proposed framework
This paper presents a development of an improved genetic algorithm
(IGA) and its application to a least-cost generation expansion planning
(GEP) problem. Least-cost GEP problem is concerned with a highly
constrained nonlinear dynamic optimization problem that can only be
fully solved by complete enumeration, a process which is computationally
impossible in a real-world GEP problem. In this paper, an improved
genetic algorithm incorporating a stochastic crossover technique and an
artificial initial population scheme is developed to provide a faster
search mechanism. The main advantage of the IGA approach is that the
“curse of dimensionality” and a local optimal trap inherent
in mathematical programming methods can be simultaneously overcome. The
IGA approach is applied to two test systems, one with 15 existing power
plants, 5 types of candidate plants and a 14-year planning period, and
the other, a practical long-term system with a 24-year planning period
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