[Show abstract][Hide abstract] ABSTRACT: With the development of home area network, residents have the opportunity to schedule their power
usage in the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy
sources are deployed in home, a home energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy
management model has been presented in which electrical and thermal appliances are jointly scheduled.
The proposed method aims at minimizing the electricity cost of a residential customer by scheduling various type of appliances considering the residents consumption behavior, seasonal probability, social random factor, discomfort index and appliances starting probability functions. In this model, the home central controller receives the electricity price information, environmental factors data as well as the resident desired options in order to optimally schedule appliances including electrical and thermal. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, photovoltaic panel, combined heat and power unit, boiler and electrical and thermal storages over a 24-h period. The results show that the scheduling of different appliances can be reached simultaneously by using the proposed formulation. Moreover, simulation results evidenced that the proposed home energy management model exhibits a lower cost and, therefore, is more economical.
Energy Conversion and Management 09/2015; DOI:10.1016/j.enconman.2015.09.017 · 4.38 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper, a robust bi-level decision-making framework is presented for distributed generation (DG) owning retailers to supply the electricity to price-sensitive customers. Uncertainties about client demand and wholesale prices are the main difficulties faced by the electricity retailer. Clients can adjust their consumption according to the retailer's selling price. A higher selling price increases retailers' profit but decreases client consumption. Hence, the retailer faces a tradeoff between the price and sales. In the proposed model, the optimal selling price and the retailer's energy-supply strategy are modeled in the lower sub-problem. According to the proposed selling price, the optimal energy consumption of price-sensitive clients is determined in the upper sub-problem. To evaluate the financial risk arising from uncertain prices, the Information Gap Decision Theory (IGDT) approach is addressed in the lower sub-problem. Additionally, the risk-based optimization problem is formulated for risk-averse and risk-taker retailers via the robustness and opportunity functions, respectively. The robustness of the optimal solution against price variations is evaluated such that the associated profit will be more than the electricity retailer's acceptable threshold. The efficiency and performance of the decision-making framework are analyzed via a case study, and the numerical results are discussed.
[Show abstract][Hide abstract] ABSTRACT: Energy crisis along with environmental concerns are some principal motivations for introducing “energy hubs” by integrating energy production, conversion and storage technologies such as combined cooling, heating and power systems (CCHPs), renewable energy resources (RESs), batteries and thermal energy storages (TESs). In this paper, a residential energy hub model is proposed which receives electricity, natural gas and solar radiation at its input port to supply required electrical, heating and cooling demands at the output port. Augmenting the operational flexibility of the proposed hub in supplying the required demands, an inclusive demand response (DR) program including load shifting, load curtailing and flexible thermal load modeling is employed. A thermal and electrical energy management is developed to optimally schedule major household appliances, production and storage components (i.e. CCHP unit, PHEV and TES). For this purpose, an optimization problem has been formulated and solved for three different case studies with objective function of minimizing total energy cost while considering customer preferences in terms of desired hot water and air temperature. Additionally, a multi-objective optimization is conducted to consider consumer's contribution to CO2, NOx and SOx emissions. The results indicate the impact of incorporating DR program, smart PHEV management and TES on energy cost reduction of proposed energy hub model.
Energy and Buildings 03/2015; 90. DOI:10.1016/j.enbuild.2014.12.039 · 2.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper presents a new dynamic approach on the expansion planning problem in power systems. First, the coordination between generation system expansion and transmission system expansion has been formulated as a mixed integer nonlinear programming (MINLP) problem. Then, it has been shown that this MINLP model cannot be efficiently solved by the traditional MINLP solvers. Since the nonlinear term comes from the multiplication of a binary variable by a continuous one, a Benders decomposition approach has been employed to convert the MINLP formulation into a mixed integer linear programming (MILP) master problem, and a linear programming (LP) sub-problem. Besides, different times of construction have been considered for different transmission and generation facilities. In addition, a clustering based algorithm has been proposed to evaluate the reliability of the system at hierarchical level II (HLII). Since this dynamic planning method is an upgraded version of a recent developed static model, the result from both methods have been also compared. A simple 6-bus test system and IEEE 30-bus system have been selected to confirm the effectiveness of the introduced method.
International Journal of Electrical Power & Energy Systems 02/2015; 65. DOI:10.1016/j.ijepes.2014.10.007 · 3.43 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: With the emergence of smart grid which has Advanced Metering Infrastructure (AMI) and microgrid running as a local energy provider for dwellings through Distributed Energy Resources (DERs), dwellers have the opportunity to schedule their in-home energy usage by themselves to reduce energy expense. Household tasks along with DER operation are scheduled according to electricity Real-Time Price (RTP) and natural gas fixed price. Various studies have shown that the lack of effective home automation systems as well as the lack of awareness among users to respond to time-varying prices are two chief obstacles in order to fully exploit the potential benefits of dynamic pricing schemes. This paper drives to handle these problems by proposing an automatic and optimal residential energy consumption scheduling technique which tries to achieve a favorable trade-off between minimizing the energy costs as well as the inconvenience for the operation of both electrical and thermal in a smart home environment. Simulation results show that the proposed scheduling method leads to significant reduction in energy costs for diverse load scenarios with the electricity demand from the grid. Therefore, the deployment of the proposed method is advantageous for both users and utilities.
Energy and Buildings 02/2015; 93. DOI:10.1016/j.enbuild.2015.01.061 · 2.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Distribution system complexity is increasing mainly due to technological innovation, renewable Distributed Generation (DG) and responsive loads. This complexity makes difficult the monitoring, control and operation of distribution networks for Distribution System Operators (DSOs). In order to cope with this complexity, a novel method for the integrated operational planning of a distribution system is presented in this paper. The method introduces the figure of the aggregator, conceived as an intermediate agent between end-users and DSOs. In the proposed method, energy and reserve scheduling is carried out by both aggregators and DSO. Moreover, Electric Vehicles (EVs) are considered as responsive loads that can participate in ancillary service programs by providing reserve to the system. The efficiency of the proposed method is evaluated on an 84-bus distribution test system. Simulation results show that the integrated scheduling of EVs and renewable generators can mitigate the negative effects related to the uncertainty of renewable generation.
Energy Conversion and Management 01/2015; 89:99–110. DOI:10.1016/j.enconman.2014.09.062 · 4.38 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: بحران انرژی یکی از جدیترین چالشهای پیش روی توسعه پایدار جامعه بشری در قرن بیست و یکم است. حدود 20 تا 30 درصد از انرژی مصرفی در ساختمانها را میتوان از طریق عملیات بهینه سازی و مدیریت کاهش داد، بنابراین پتانسیل عظیمی برای صرفه جویی در انرژی مصرفی در ساختمان از طریق بهره برداری و مدیریت کارآمد وجود دارد. در این مطالعه با زمانبندی یکپارچه منابع انرژی شامل شبکه توزیع، صفحات خورشیدی، باتری، توربین بادی و واحدهای تولید همزمان برق و حرارت همزمان با زمان بندی وسایل الکتریکی و حرارتی به این مهم دست یافت. تابع هدف حداقل نمودن هزینه برق خریداری شده از شبکه و گاز طبیعی مصرفی و همچنین کاهش اوج دریافتی از شبکه با در نظر گرفتن قیود تجهیزات، شرایط آب و هوایی، قیمتهای انرژی و عدم قطعیت منابع انرژی تجدیدپذیر است. در این پژوهش سیستم مدیریت انرژی هوشمند طراحی میشود تا برنامه ریزی روزانه به دست آید. مدلی پیشنهاد خواهد شد که به برنامهریزی مصرف توان خانه های هوشمند، بهینه سازی بهرهبرداری از منابع تولید انرژی و زمانبندی وسایل الکتریکی و حرارتی همراه با در نظر گرفتن قیود تجهیزات به صورت یک مدل برنامه ریزی خطی عدد صحیح ترکیبی (MILP) با هدف کمینه کردن هزینه های انرژی میپردازند.
[Show abstract][Hide abstract] ABSTRACT: In this paper a stochastic operational scheduling method is proposed to schedule energy and reserve in a smart distribution system with high penetration of wind generation. The wind power and demand forecast errors are considered in this approach and the reserve is furnished by both main grid generators and responsive loads. The consumers participate in both energy and reserve scheduling. A Demand Response Provider (DRP) aggregates loads reduction offers in order to facilitate small and medium loads participation in demand response program. The scheduling approach is tested on an 83-bus distribution test system over a 24-h period. Simulation results show that the proposed stochastic energy and reserve scheduling with demand response exhibits a lower operation cost if compared to the deterministic scheduling.
International Journal of Electrical Power & Energy Systems 12/2014; 63:218–225. DOI:10.1016/j.ijepes.2014.05.062 · 3.43 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Demand side participation is one of the important resources that help the operator to schedule generation and consumption with lower cost and higher security. Customers can participate in both energy and reserve operational scheduling and earn benefit from reducing or shifting their consumption. In this paper, a novel stochastic energy and reserve scheduling method for a microgrid (MG) which considers various type of demand response (DR) programs is proposed. In the proposed approach, all types of customers such as residential, commercial and industrial ones can participate in demand response programs which will be considered in either energy or reserve scheduling. Also, the uncertainties related to renewable distributed generation are modeled by proper probability distribution functions and are managed by reserve provided by both DGs and loads. The proposed method was tested on a typical MG system comprising different type of loads and distributed generation units. The results demonstrate that the adoption of demand response programs can reduce total operation costs of a MG and determine a more efficient use of energy resources.
International Journal of Electrical Power & Energy Systems 12/2014; 63:523-533. DOI:10.1016/j.ijepes.2014.06.037 · 3.43 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this study, a substantial idea has been reviewed which is useful in the investigation of the planning uncertainties. The concept relies on the optimality of an expansion plan in different conditions rather than the condition it has been optimised for. The method is developed in a manner that can be used in all sub-systems (i.e. generation, transmission and distribution) expansion planning. However, in this study the idea has been assessed for generation expansion planning (GEP). Cost and reliability have been considered as two major objectives of the planning and robustness has been added as a supplementary objective. The method can deal with uncertainty in both coefficients of the objective functions and the constraints. Two GEP models, one static and the other dynamic, have been proposed to examine the performance of the method in the uncertainty handling. In addition, the efficiency of the Taguchi's orthogonal array testing method has been compared with Monte Carlo simulation in the scenario generation. Two case studies have been provided to simplify the justification on the efficiency of the method.
IET Generation Transmission & Distribution 12/2014; 8(12). DOI:10.1049/iet-gtd.2013.0674 · 1.35 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This study addresses clarification regarding total payment function calculation in reactive power market. One of the main sources of reactive power in transmission network is synchronous generator. Capability curves of reactive power produced by synchronous generators are surveyed in this study. Meanwhile, using these curves, all the costs of one synchronous generator to inject or absorb reactive power are calculated. These costs include an availability cost and cost of losses as well as cost of losing the sale opportunity of electrical energy because of production or absorption of reactive power. The IEEE 24-bus reliability test system is used to illustrate the proposed cost calculation technique in reactive power market.
IET Generation Transmission & Distribution 11/2014; 8(11). DOI:10.1049/iet-gtd.2013.0901 · 1.35 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Wind and solar energy introduced significant operational challenges in a Microgrid (MG), especially when renewable generations vary from forecasts. In this paper, forecast errors of wind speed and solar irradiance are modeled by related probability distribution functions and then, by using the Latin hypercube sampling (LHS), the plausible scenarios of renewable generation for day-head energy and reserve scheduling are generated. A two-stage stochastic objective function aiming at minimizing the expected operational cost is implemented. In the proposed method, the reserve requirement for compensating renewable forecast errors is provided by both responsive loads and distributed generation units. All types of customers such as residential, commercial and industrial ones can participate in demand response programs which are considered in either energy or reserve scheduling. In order to validate the proposed methodology, the proposed approach is finally applied to a typical MG and simulation results are carried out.
Energy Conversion and Management 10/2014; 86:1118–1127. DOI:10.1016/j.enconman.2014.06.078 · 4.38 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) into the electric power system may bring up many technical issues. The power system may put at risk the security and reliability of operation due to intermittent nature of renewable generation and uncontrolled charging/discharging procedure of EVs. In this paper, an energy resources management model for a microgrid (MG) is proposed. The proposed method considers practical constraints, renewable power forecasting errors, spinning reserve requirements and EVs owner satisfaction. A case study with a typical MG including 200 EVs is used to illustrate the performance of the proposed method. The results show that the proposed energy resource scheduling method satisfies financial and technical goals of parking lot as well as the security and economic issues of MG. Moreover, EV owners could earn profit by discharging their vehicles’ batteries or providing the reserve capacity and finally have desired State Of Charge (SOC) in the departure time.
Energy Conversion and Management 10/2014; 86:745–755. DOI:10.1016/j.enconman.2014.06.044 · 4.38 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper presents a risk-based decision-making framework for the Distributed Generation (DG)-owning retailer to determine the optimal participation level in the forward and the day-ahead electricity markets, as well as the optimal scheduling of DG units. The energy price in the day-ahead market is very volatile and varies every hour. Also, unpredicted failures of DG units may impose a great financial loss on the retailer. Therefore, the retailer has to evaluate the effects of uncertain parameters to hedge the financial loss. In this paper, the financial risk associated with the uncertain prices is evaluated using the chance constraint optimization method. The normal density function is applied to model the probabilistic realizations of uncertain prices, and scenarios are generated via the scenario tree technique. Moreover, the availability of generation units is modeled by the availability probability. The proposed risk-based framework allows the retailer to determine the optimal strategy at the given risk level. The objective-function of the presented model is based on maximizing the expected profit in a way that ensures the specific profit constraint will be satisfied at the operation period with a defined probability. The performance and efficiency of the presented decision-making framework are analyzed on the sample DG-owning retailer, and the optimal framework is simulated under different risk levels. (C) 2014 AIP Publishing LLC.
Journal of Renewable and Sustainable Energy 09/2014; 6(5). DOI:10.1063/1.4896786 · 0.90 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Increasing energy prices due to energy crisis and the environmental pollution lead to more awareness of both thermal and electrical energy efficiency.
The hybrid power systems along with different renewable energy sources and storage systems is considered. The problem solution is an optimal decision while considering the integration of renewable energy sources in a micro-grid and also taking into account the both wind and solar resources as well as accurate electrical and thermal demand predictions. The microgrid is operated in grid-connected mode.
The multi-objective function consists of energy cost and emission rate as well. Simulations of different scenarios show that both objectives can be reached simultaneously.
the 10th International Energy conference (IEC); 08/2014
[Show abstract][Hide abstract] ABSTRACT: This study formulates a security-constrained multi-objective framework with unit commitment in multi-area electricity
markets for day-ahead joint market-clearing. The dynamic and inertial characteristics of the power system are derived and
incorporated into the market-clearing procedure in order to preserve power system security from the frequency viewpoint. In
addition, two novel objective functions (tie-flow deviation index and Rocof index) besides the frequency-dependent social
welfare and frequency excursion index have been defined to control the static frequency, rate of change of frequency (Rocof)
and also the tie-line power flows, following the occurrence of a contingency. Comprehensive analysis tools for multi-area
power systems and also pre- and post-contingency intervals are presented to verify the characteristics of the proposed model.
The developed multi-objective programming is analysed through two case studies, a three-area system scheduled over 1 h and
the IEEE two-area reliability test system over 24 h. It has been shown that the scheduling of energy and reserve services can
be performed more effectively if the system frequency is considered in the market-clearing process. The proposed model can
reconcile the need for reasonable total generation cost with concern for the independent system operator’s responsibilities
about the pre- and post-contingency tie-line power flow control and system security.
IET Generation Transmission & Distribution 06/2014; 8(12). DOI:10.1049/iet-gtd.2013.0866 · 1.35 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The development of smart grids offers new opportunities to improve the efficiency of operation of Distributed Energy Resources (DERs) by implementing an intelligent Distribution Management System (DMS). The DMS consists of application systems that are used to support the DERs management undertaken by a Distribution System Operator (DSO). In this paper, a conceptual model for a Demand Response Management System (DRMS), conceived as an application system of a DMS, is presented. Moreover, an optimization tool, able to consider the available DERs (conventional or renewable Distributed Generations (DGs) and demand response) is proposed. The optimization tool uses a stochastic multi-objective method in order to schedule DERs and aims at minimizing the total operational costs and emissions while considering the intermittent nature of wind and solar power as well as demand forecast errors. In order to facilitate small and medium loads participation in demand response programs, a Demand Response Provider (DRP) aggregates offers for load reduction. The proposed scheduling approach for DERs is tested on a 69-bus distribution test system over a 24-h period.
Electric Power Systems Research 06/2014; 111:156–168. DOI:10.1016/j.epsr.2014.02.021 · 1.75 Impact Factor