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Simulation Tools and Optimization Algorithms for Efficient Energy Management in Neighborhoods
Abstract
As detailed in the EPN process of Chapter 3, the Neighborhood Energy Manager (NEM) and owner/owners select the potential business models for the Neighborhood and have available a number of energy services, such as optimization, real-time monitoring, and forecasting to select for deployment on their Neighborhood energy management platform.
Having identified business cases for the Neighborhood and having defined and performed a suitable benchmarking process, a Neighborhood-level objective can be formulated in terms of optimization accounting for both the economic criterion and environmental impact (energy cost and CO2 emissions). In this context, modeling and simulation tools can be used as decision support tools for planning and operation of energy
systems in buildings as well as proof of concept of energy optimization algorithms before carrying out field tests.
In this chapter different schemas for energy optimization in Neighborhoods are proposed and a Modelica-based library is used to efficiently simulate and test the optimization actions in city districts.
The Neighborhood energy optimization algorithms perform an energy price driven scheduling of the generation and storage equipment to reduce the Neighborhood netload seen from the grid side while keeping Neighborhood pollution emissions below a given threshold.
The Modelica-based library supports the field tests with more extensive assessments and offers numerous components for a multiphysics simulation of Neighborhood energy system. While a first version of the library developed by RWTH is available in GitHub at https://github.com/RWTH-EBC/AixLib, a second version enriched with the electrical elements from RWTH and Dynamic Phasors will be offered in the same
server as a separate release.
... Various techniques are employed in such studies at urban scale, including numerical modeling, simulations, and mathematical optimization. Energy simulation tools include a variety of software such as EnergyPlus, TRNSYS, eQUEST, Modelica, CitySim, CPLEX, etc. [10,11] . In addition to the evaluation of performance aspects of neighborhoods, the optimization of design and operating parameters is a prominent component of neighborhood development. ...
... In addition to the evaluation of performance aspects of neighborhoods, the optimization of design and operating parameters is a prominent component of neighborhood development. Neighborhood optimization can be categorized into centralized optimization and hierarchical two-level optimization [10] . Both of these optimization techniques are related to the operating costs, where in centralized optimization, the collective neighborhood operation cost is optimized, whereas two-level optimization optimizes the costs at building as well as neighborhood levels. ...
This paper presents optimization technique of residential and commercial buildings mixture within a mixed-use neighborhood, for enhanced energy performance and less GHG emission. The study, conducted for a Northern high latitude location, investigates several parameters related to building types, their density, floor area as well as ratio of specific building types to the total built area. Several methods are employed in the investigation, including energy simulations, statistical methods, optimization and decision making. A selection method of single optimal neighborhood scenario, based on the analysis of seasonal average hourly load is proposed. The results show that an optimal commercial to built land area ranges between 22% and 32%, with the remaining area dedicated to residential buildings. Multiple combinations of residential buildings that enhance energy performance while reducing GHG emissions are formed by 50% of detached houses and 50% of combined townhouses and apartment units (with different ratios). On the commercial side, near optimal combinations of commercial buildings area is constituted by 46–75% offices, 18–45% retails, and 6–9% supermarkets. Optimizing the hourly energy load profile, a scenario combining commercial land area of 25% with equal composition of office and retail buildings (47.5%), to residential area consisting of equal proportion of single detached and townhouses (48% each) can be selected.
... The main result shows that the viability of PEDs is strongly related to the floor space index of the neighbourhood, determining the available space for PV power generation compared to the amount of energy demanded. In Ref. [31], the authors elaborate on two optimisation approaches for Energy Positive Neighbourhoods, which are highly similar to PEDs from a technical perspective. While one approach assumes a centrally managed district, the other includes a hierarchical two-level optimisation. ...
To reduce CO2 emissions, the European Commission aims at having 100 Positive Energy Districts (PEDs) planned, developed or established by 2025. A PED annually exports more energy than it imports from the local grid. Because of Europe's diversity, this study aims to indicate where in the EU and under which tariff circumstances an electrified PED will likely thrive most. To do so, the work uses a tailor-made mixed-integer linear programming model to optimise electrified PED solutions and compare them to the respective status quo for various representative zone-tariff parameter combinations. Results indicate that the optimal potential for PEDs is in southern Europe, with a dynamic electricity tariff and where previously no district heating was used. Under those circumstances, the PED concept could save around 84% of carbon emissions while being more economical over the project horizon. Pricing of CO2 emissions of energy services additionally nudges towards PED implementation. By limiting the power exchange of the PED with the grid, some of the negative grid impacts can be reduced. This study provides an essential insight into where in Europe a PED could be a sensible addition compared to where other decarbonisation approaches might be more beneficial.
... Concerning energy management, the literature is replete with algorithms and tools for simulation and optimization in regard to efficient energy management in building heating systems [22]. An investigation on the specific issue of advanced control system design for energy efficient buildings was presented by Ma et al. in 2012 [23]. ...
In the context of a world energy crisis, the only solution to control the situation is in the management of energy. One of the most important management keys is the optimization of electrical components. This article presents a complete numerical and experimental study aiming for the optimization of electrical water heaters for household use. The optimization conceives the minimization of energy consumption simultaneously with the minimization of time to heat water. Firstly, a thermal model well adapted for the case of heaters is constructed and validated experimentally and then a parametric study is conducted covering all the input power, the volume and the external area of the heater. Results are promising, showing significant energy savings are possible with an optimum setting of these parameters, thus presenting a firm tool for the optimization of heaters.
... In this work, the authors suggested a pricing model which aims at maintaining low operational costs while utilizing solar generation, stationary battery systems and mobile EV storage as much as possible. The authors in [23] provided a framework for energy optimization in neighborhoods. The authors considered the existence of multiple resources and buildings. ...
With the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in overlapped schedules that can cause voltage deviations in their microgrid. This paper proposes a decentralized management framework that is able to minimize the total electricity costs of a commercial microgrid and limit the voltage deviations. The proposed scheme is a two-level optimization where the lower level ensures the thermal comfort inside the buildings while the upper level consider system-wise constraints and costs. The decentralization of the framework is able to maintain the privacy of individual buildings. Multiple data-driven building models are developed and compared. The effect of the building modeling on the overall operation of coordinated buildings is discussed. The proposed framework is validated on a modified IEEE 13-bus system with different connected types of commercial buildings. The results show that coordinated optimization outperforms the commonly used commercial controller and individual optimization of buildings. The results also show that the total costs are greatly affected by the building modeling.
... UBEMs have emerged in recent years as efficient hybrid of top-down statistical and bottom-up engineering approaches [14][15][16][17] and they are expected to become the main planning tool for energy utilities, municipalities, urban planners and other professionals [18][19][20][21]. Both the UBEMs and the virtual city maps are considered as the new generation of tools that based on the digital twins concept, allow the analysis and monitoring of large urban areas and built stock [22][23][24][25]. The UBEM allows the multilevel integration of several sources of information, the energy simulation of building and characterization of buildings, and as a result, the generation of new essential knowledge and new scenarios for urban regeneration [26][27][28]. ...
In this paper, we provide a view of the ongoing PEDRERA project, whose main scope is to design a district simulation model able to set and analyze a reliable prediction of potential business scenarios on large scale retrofitting actions, and to evaluate the overall co-benefits resulting from the renovation process of a cluster of buildings. According to this purpose and to a Positive Energy Districts (PEDs) approach, the model combines systemized data—at both building and district scale—from multiple sources and domains. A sensitive analysis of 200 scenarios provided a quick perception on how results will change once inputs are defined, and how attended results will answer to stakeholders’ requirements. In order to enable a clever input analysis and to appraise wide-ranging ranks of Key Performance Indicators (KPIs) suited to each stakeholder and design phase targets, the model is currently under the implementation in the urbanZEB tool’s web platform.
... Even though the use of machine learning techniques is relatively recent, model predictive control (MPC) and all its flavors, produced a large number of publications in the field of optimal control for energy storage systems. A complete review on control of storage systems can be found in [1][2][3][4] with particular reference to MPC approaches. Even though MPC is able to reach optimal or quasi-optimal solutions, its implementation for complex systems is challenging. ...
Deep Reinforcement Learning (DRL) proved to be successful for solving complex control problems and has become a hot topic in the field of energy systems control, but for the particular case of thermal energy storage (TES) systems, only a few studies have been reported, all of them with a complexity degree of the TES system far below the one of this study. In this paper, we step forward through a DRL architecture able to deal with the complexity of an innovative hybrid energy storage system, devising appropriate high-level control operations (or policies) over its subsystems that result optimal from an energy or monetary point of view. The results show that a DRL policy in the system control can reduce the system operating costs by more than 50%, as compared to a rule-based control (RBC) policy, for cooling supply to a reference residential building in Mediterranean climate during a period of 18 days. Moreover, a robustness analysis was carried out, which showed that, even for large errors in the parameters of the system simulation models corresponding to an error multiplying factors up to 2, the average cost obtained with the original model deviates from the optimum value by less than 3%, demonstrating the robustness of the solution over a wide range of model errors.
... Bilevel optimization [6] applies formal mathematical techniques to characterize and find the equilibrium in game-theoretical decision situations with two fully rational parties with given constraints and objectives, and a well-defined serial decision workflow. This is different from generic hierarchical modeling techniques that analyze the interplay of two or more decision makers, typically by solving the problems faced by the individual parties one by one, and constructing the overall outcome by assuming some coordination mechanism between them, often using simulation techniques; see, e.g., [7]. This paper considers bilevel optimization strictly in the former sense. ...
This paper investigates bilevel optimization models for demand response management, and highlights the often overlooked consequences of a common modeling assumption in the field. That is, the overwhelming majority of existing research deals with the so-called optimistic variant of the problem where, in case of multiple optimal consumption schedules for a consumer (follower), the consumer chooses an optimal schedule that is the most favorable for the electricity retailer (leader). However, this assumption is usually illegitimate in practice; as a result, consumers may easily deviate from their expected behavior during realization, and the retailer suffers significant losses. One way out is to solve the pessimistic variant instead, where the retailer prepares for the least favorable optimal responses from the consumers. The main contribution of the paper is an exact procedure for solving the pessimistic variant of the problem. First, key properties of optimal solutions are formally proven and efficiently solvable special cases are identified. Then, a detailed investigation of the optimistic and pessimistic variants of the problem is presented. It is demonstrated that the set of optimal consumption schedules typically contains various responses that are equal for the follower, but bring radically different profits for the leader. The main procedure for solving the pessimistic variant reduces the problem to solving the optimistic variant with slightly perturbed problem data. A numerical case study shows that the optimistic solution may perform poorly in practice, while the pessimistic solution gives very close to the highest profit that can be achieved theoretically. To the best of the authors’ knowledge, this paper is the first to propose an exact solution approach for the pessimistic variant of the problem.
... In order to verify the optimal values achieved by the proposed scheme, the centralized economic dispatch algorithm based on mathematical programming according to [36] is developed, which can be formulated as follows: where PD,i is the load at bus i, and Y is the admittance matrix corresponding to the electrical network. The optimization model (46) can be solved by mathematical programming such as quadratically constraint quadratic programming. ...
In this paper, a distributed economic dispatch scheme considering power limit is proposed to minimize the total active power generation cost in a droop-based autonomous direct current (DC) microgrid. The economical dispatch of the microgrid is realized through a fully distributed hierarchical control. In the tertiary level, an incremental cost consensus-based algorithm embedded into the economical regulator is utilized to search for the optimal solution. In the secondary level, the voltage regulator estimating the average voltage of the DC microgrid is used to generate the voltage correction item and eliminate the power and voltage oscillation caused by the deviation between different items. Then, the droop controller in the primary level receives the reference values from the upper level to ensure the output power converging to the optimum while recovering the average voltage of the system. Further, the dynamic model is established and the optimal communication network topology minimizing the impact of time delay on the voltage estimation is given in this paper. Finally, a low-voltage DC microgrid simulation platform containing different types of distributed generators is built, and the effectiveness of the proposed scheme and the performance of the optimal topology are verified.
An effective quantification of the energy absorbed and supplied by latent heat thermal energy storage (LHTES) units is critical to maximise their use within thermal systems. An effective control of the charging and discharging processes of these units demands an accurate estimation of the state-of-charge (SoC). However, a direct and reliable SoC estimation requires incorporating internal sensors to monitor the temperature gradient of the phase change material (i.e. the storage medium), resulting in higher instrumentation costs and technical specifications. These issues may be relieved by adopting state observers for SoC estimation to drastically reduce the number of measurements. This paper bridges this gap by presenting a novel and direct method for estimating the SoC of LHTES units, both for heating and cooling applications, based on a non-linear state observer. The observer is based on a simple one-dimensional dynamic model of the thermal store and the thermophysical properties of the storage medium and the heat transfer fluid, which are usually provided by manufacturers. This enables the estimation of the internal temperatures of the LHTES unit and, in turn, SoC calculation. The observer implementation is simple as it requires three measurements only as input variables (i.e. the mass flow rate and the input and output temperatures of the heat transfer fluid). The SoC estimation approach is assessed through dynamic simulations of two LHTES units: one for a heating application and one for a cooling application. The results show that the SoC can be estimated with root mean square and mean absolute errors of less than 4.6% and 3.62%, respectively, compared with experimental measurements.
In this research, a novel open-source methodology is presented to perform building cluster and neighborhood simulation by integrating individual archetypes building energy models (BEM). This methodology merges various building Energy Plus models employing a programming language. For the demonstration of generalized methodology, Matlab is used, however, methodology can be implemented by other programming languages such as Python. A given BEM can be employed multiple times to avoid its remodeling in a traditional cluster model setup. The proposed workflow is successfully validated for low, mid, and high-rise mixed-use building clusters with a maximum deviation of ± 2.70% in various annual performance parameters compared to the summation of individual BEM simulations. A sensitivity analysis is also performed to quantify the root mean square deviation (RMSD) between cluster and individual BEM simulations by varying building distance to height ratios along north-south and east-west axes. A significance index on a ten-point scale is proposed to evaluate the importance of cluster simulations over the individual BEM simulations (where the sum of individual BEM results can be used to replace cluster simulations). For instance, the significance index found is to be highest for high-rise building cluster, suggesting thus that building cluster simulations yield more adequate results than individual BEM simulations.
Urban areas have been responsible for the majority of the European Union (EU)-wide primary energy demand and CO2 emissions. To address this issue, the European Union introduced the concept of Positive Energy Districts (PEDs). PEDs are required to have an annual positive primary energy balance. However, if directly addressed in the literature, this energy balance only includes annually fixed primary energy factors and often neglects grid impacts. To bridge this gap, this work proposes a mathematical optimisation approach for PEDs, working towards an open-source model. The model’s main novelty is an hourly primary energy balance constraint. The performed case study on the island La Palma for both an urban and a rural neighbourhood show that the PED concept has a higher net present value (NPV) than solely buying electricity from the grid in all feasible cases. Depending on the space available for PV installations, the NPV increases between 29 and 31% and 25–27% for the rural and urban PED scenarios, respectively. However, in the scenarios with reduced grid impact, the NPV decreases due to the expensive battery installations. Comparing the significantly fossil-based electricity grid mix of La Palma with the renewable-based one of El Hierro shows that the primary energy-based optimisation has more room for flexibility in the high renewable mix. While the dynamic primary energy balance constraint appears promising for operational optimisation, the allocation of correct primary energy factors is crucial.
In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. In spite of the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. In order to optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors meanwhile the last operation mode, considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm is applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach
Active distribution system management (ADSM) aims at optimally operating distribution network assets with a high penetration of distributed energy resources (DERs) while taking into account operational uncertainties, market constraints, and scheduled power flows at the interface with the transmission system. A novel framework for ADSM, which incorporates a dual-horizon rolling scheduling model based on dynamic ac optimal power flow, is proposed in this paper. In the first stage (planning), energy import/export is committed for given times ahead at the grid supply point considering local variable generation and load forecasts. At the second stage (operation) deviations from the schedules are minimised by controlling various assets and DER, including electrical energy storage (EES). We demonstrate how crucial it is to properly consider uncertainties (for instance, associated with forecasts) over different time scales. Case study results on a real U.K. distribution network show that the proposed ADSM approach can provide credible operational strategies to maximise renewable penetration, minimize deviations from time-ahead schedules, and estimate the required level of local reserves from dispatchable generation and EES while realistically accounting for uncertainty. The framework and model formulation proposed can thus be seen as a key tool to facilitate the transition from distribution network operators to distribution system operators and their interaction with transmission system operators.
This paper aims to set up a probabilistic framework to assess the value of a portfolio of demand response (DR) customers under both operational (short-term) and planning (long-term) uncertainties through real options (ROs) modeling borrowed from financial theory. In an operational setting, DR is considered as an RO contract allowing an aggregator seeking to maximize its revenue to sell flexible demand in the day-ahead market and balance its energy portfolio in the balancing markets. Sequential Monte Carlo simulations (SMCS) are used to value DR activation decisions based on market price evolutions. These decisions combine DR physical characteristics and portfolio scheduling optimization, whereby the aggregator chooses to exercise only the contracts probabilistically leading to a profit, also considering the physical payback effects of load recovery. Sensitivity of profits to changing market conditions and payback characteristics is also assessed. In an investment setting, subject to long-term uncertainties, the value of an investment in DR-enabling technology is quantified through the Datar--Mathews RO approach that applies hybrid SMCS and scenario analysis. The results show how the flexibility value of DR can be highlighted by modeling it as RO, particularly in high volatile markets, and how realistic inclusion of payback characteristics significantly decrease the benefits estimated for DR. In addition, the proposed RO framework generally allows hedging of the risks incurred under long-term and short-term uncertainties.
This paper presents a two-stage stochastic programming model for provision of flexible demand response (DR) based on thermal energy storage in the form of hot water storage and/or storage in building material. Aggregated residential electro-thermal technologies (ETTs), such as electric heat pumps and (micro-) combined heat and power, are modeled in a unified nontechnology specific way. Day-ahead optimization is carried out considering uncertainty in outdoor temperature, electricity and hot water consumption, dwelling occupancy, and imbalance prices. Building flexibility is exploited through specification of a deadband around the set temperature or a price of thermal discomfort applied to deviations from the set temperature. A new expected thermal discomfort (ETD) metric is defined to quantify user discomfort. The efficacy of exploiting the flexibility of various residential ETT following the two approaches is analyzed. The utilization of the ETD metric to facilitate quantification of the expected total (energy and thermal discomfort) cost is also demonstrated. Such quantification may be useful in the determination of DR contracts set up by energy service companies. Case studies for a U.K. residential users’ aggregation exemplify the model proposed and quantify possible cost reductions that are achievable under different flexibility scenarios.
Most commentators expect improved energy efficiency and reduced energy demand to provide the dominant contribution to tackling global climate change. But at the global level, the correlation between increased wealth and increased energy consumption is very strong and the impact of policies to reduce energy demand is both limited and contested. Different academic disciplines approach energy demand reduction in different ways: emphasising some mechanisms and neglecting others, being more or less optimistic about the potential for reducing energy demand and providing insights that are more or less useful for policymakers. This article provides an overview of the main issues and challenges associated with energy demand reduction, summarises how this challenge is ‘framed’ by key academic disciplines, indicates how these can provide complementary insights for policymakers and argues that a ‘sociotechnical’ perspective can provide a deeper understanding of the nature of this challenge and the processes through which it can be achieved. The article integrates ideas from the natural sciences, economics, psychology, innovation studies and sociology but does not give equal weight to each. It argues that reducing energy demand will prove more difficult than is commonly assumed and current approaches will be insufficient to deliver the transformation required.
Microgrid is a key enabling solution to future smart grids by integrating distributed renewable generators and storage systems to efficiently serve the local demand. However, due to the random and intermittent characteristics of renewable energy, new challenges arise for the reliable operation of microgrids. To address this issue, we study in this paper the real-time energy management for a single microgrid system that constitutes a renewable generation system, an energy storage system, and an aggregated load. We model the renewable energy offset by the load over time, termed net energy profile, to be practically predictable, but with finite errors that can be arbitrarily distributed. We aim to minimize the total energy cost (modeled as sum of time-varying strictly convex functions) of the conventional energy drawn from the main grid over a finite horizon by jointly optimizing the energy charged/discharged to/from the storage system over time subject to practical load and storage constraints. To solve this problem in real time, we propose a new off-line optimization approach to devise the online algorithm. In this approach, we first assume that the net energy profile is perfectly predicted or known ahead of time, under which we derive the optimal off-line energy scheduling solution in closed-form. Next, inspired by the optimal off-line solution, we propose a sliding-window based online algorithm for real-time energy management under the practical setup of noisy predicted net energy profile with arbitrary errors. Finally, we conduct simulations based on the real wind generation data of the Ireland power system to evaluate the performance of our proposed algorithm, as compared with other heuristically designed algorithms, as well as the conventional dynamic programming based solution.
In this paper, the optimum design for a microgrid system is proposed. The microgrid is independent from the power grid, operating in a stand-alone state. Wind, solar, diesel generators and batteries are distributed resources supplying power to the microgrid. The design is optimized on both cost considerations and performance of the power system. Sensitivity analysis is performed in order to explore the effect of uncertainties. Case study and simulation results are presented and discussed in this paper.
The purpose of this paper is to review the application of complexity science methods in understanding energy systems and system change. The challenge of moving to sustainable energy systems which provide secure, affordable and low-carbon energy services requires the application of methods which recognise the complexity of energy systems in relation to social, technological, economic and environmental aspects. Energy systems consist of many actors, interacting through networks, leading to emergent properties and adaptive and learning processes. Insights on these type of phenomena have been investigated in other contexts by complex systems theory. However, these insights are only recently beginning to be applied to understanding energy systems and systems transitions.
In order to take full advantage of distributed generators, an evolution of the classical power system organization and management is also necessary. An aggregator of a residential urban electrical network can be considered by the distribution system operator as a stakeholder, which is able to control a cluster of local generators and loads with technical constraints for the connection with the remaining distribution grid and commercial contracts with outer electrical producers. This paper is focused on the design of the microgrid central energy management system which relies on a day-ahead operational planning and an online adjustment procedure during the operation. A dynamic programming-based algorithm is derived to solve the unit commitment problem with a multiobjective function in order to reduce the economic cost and CO2 equivalent emissions. The proposed energy management system is implemented into a supervisory control and data acquisition (SCADA) and tested by using a hardware-in-the-loop simulation of the urban network. Economic and environmental gains are evaluated.
In this paper, a comprehensive dedicated framework is set up to analyze distributed multi-generation (DMG) systems for the purpose of identifying and quantifying their potential to participate in real-time demand response (DR) programmes. At first, flexibility of DMG systems with multiple interconnected plant components is exploited to identify the optimal operational strategy in the presence of half-hourly pricing. Then, the costs and benefits of providing further real-time DR are assessed by taking into account different energy shifting strategies. The novel concept of electricity shifting potential is introduced to establish the upper limit to the possible reduction of the electricity flowing from the electrical grid to the DMG system. The maximum profitable energy shifting that can be activated in the presence of given DR incentives is established on the basis of a DR profitability map. The key point is that energy shifting can be deployed inside the local DMG system to respond to given DR signals without reducing the users' energy demand and thus without affecting their comfort level. Examples of real-time DR for a trigeneration system referring to half-hourly periods during selected Summer and Winter days are illustrated and discussed.
MES (multi-energy systems) whereby electricity, heat, cooling, fuels, transport, and so on optimally interact with each other at various levels (for instance, within a district, city or region) represent an important opportunity to increase technical, economic and environmental performance relative to “classical” energy systems whose sectors are treated “separately” or “independently”. This performance improvement can take place at both the operational and the planning stage. While such systems and in particular systems with distributed generation of multiple energy vectors (DMG (distributed multi-generation)) can be a key option to decarbonize the energy sector, the approaches needed to model and relevant tools to analyze them are often of great complexity. Likewise, it is not straightforward to identify performance metrics that are capable to properly capture costs and benefits that are relating to various types of MES according to different criteria. The aim of this invited paper is thus to provide the reader with a comprehensive and critical overview of the latest models and assessment techniques that are currently available to analyze MES and in particular DMG systems, including for instance concepts such as energy hubs, microgrids, and VPPs (virtual power plants), as well as various approaches and criteria for energy, environmental, and techno-economic assessment.
The vast installation of intermittent energy sources (especially wind) has distorted the normal strict pattern of the net demand (demand minus RES production), increasing the importance of real-time balancing markets, which handle efficiently imbalances between supply and demand. In this paper, the incorporation of demand response bids within a real-time balancing market is modeled. The price signal, to which the demand responds, is a price derived from the total cost incurred by increasing or decreasing power of generation units, with a pay-as-bid pricing scheme. The demand price is defined implicitly as a function of the upward and downward supply offers. The proposed balancing market clearing model is formulated as a mixed complementarity problem. All inter-zonal and intra-zonal dc transmission constraints are incorporated to the problem, thus making it a problem of interregional balancing, and nodal prices are derived from its clearing. The credits/debits to generators for providing balancing energy are balanced in zonal and system level with the debits/credits of elastic and inelastic demand entities. The proposed model can be useful for designing future real-time balancing markets, in view of the forthcoming active participation of price-responsive demand and the significant RES penetration targets set by most countries.
The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL~2 ontology to describe sensors and observations --- the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.
Accurately determining the user's position is con-sidered the key enabling technology for the provision of location based services on smartphone devices. The most promising ap-proaches to date for achieving accurate indoor coverage are based on WiFi fingerprinting in combination with complex estimation and filtering algorithms. In order to meet these computational requirements for a large number of devices a localisation system must be inherently distributed to provide real time responsiveness to all users at all times. Furthermore, to make such a system commercially feasible those computational resources sometimes cannot be maintained by the localisation service provider itself but have to be dynamically allocated in the cloud always based on current and not on peak demand, which depending on the application can vary significantly and thereby render static systems commercially infeasible in these cases. In this paper we will propose such a cloud based architecture enabling the immediate demand driven provision of computational resources to connected devices.
The current development of distribution networks into active and smarter systems has been facilitated by the integration of novel technologies such as Microgrids (MGs) and Distributed Energy Resources. These new concepts are expected to bring about significant economic benefits at the distribution level in the form of network reinforcement deferral and increased reliability levels, among others. In this context, this paper aims at shedding light on the impacts that the use of MGs for post-contingency distribution network management can have on reliability. For this purpose, a reliability assessment tool based on sequential Monte Carlo Simulations (MCS) is presented and used for the evaluation of a real distribution network, while a framework to implement the MG services is also proposed. The results show that reliability improvements for MG users can be remarkable, while external network users and the network operator also perceive benefits. The former experience higher reliability levels, and the latter experience lower network reinforcement needs when connecting additional customers due to the services provided by MGs.
Current policy debates and energy scenarios for the UK highlight the different possible ways of transforming the energy system in order to meet long-term national policy goals, including those of building a low carbon economy, achieving energy security and affordability, and mitigating environmental impacts. Although there has been much previous research on what publics think about specific ways of producing or consuming energy, we know far less about public perceptions, attitudes and values when elicited in relation to whole energy system change as an interconnected set of transformations in the systems of supply, demand, infrastructure and human behaviour. Greater understanding of public acceptability of whole energy system change will present both opportunities, and also highlight challenges, for the delivery of UK energy policy and transitions. The research had three empirical phases: interviews with key stakeholders, a series of six in-depth deliberative workshops held with publics in England, Scotland and Wales, and a nationally representative survey (Great Britain, n=2,441). This report represents a synthesis of key findings drawn from the two core datasets relating to public perceptions and preferences i.e. the workshops and the survey. The core conclusion from the research is that the British public wants and expects change with regard to how energy is supplied, used and governed. Members of the public are positive about the need for energy system change and they do not prioritise the demand side over the supply side, or vice versa, as being in greater need of change. Within this, the research has illuminated a wide range of novel insights on public attitudes regarding: energy policy drivers; elements of energy system change; and the underlying values and principles that people draw on when engaging with this issue.
The concept of Demand Response (DR) generally concerns methodologies, technologies and commercial arrangements that could allow active participation of consumers in the power system operation. The primary aim of DR is thus to overcome the “traditional” inflexibility of electrical demand and, amongst others, create a new powerful tool to maximize deployment of renewable energy sources as well as provide active network management solutions to help reducing the impact of limited grid capabilities.
DR allows consumers to actively participate in power system operation, thus bringing new opportunities in emerging energy markets as well as tangible system benefits. In this sense, DR is considered one of the key enablers of the Smart Grid concept. However, DR also poses a number of challenges, particularly when “active demand” is connected to the Low Voltage network, thus affecting all the actors involved in the electricity chain.
This book presents for the first time a comprehensive view on technical methodologies and architectures, commercial arrangements, and socio-economic and regulatory factors that could facilitate the uptake of DR. The work is developed in a systematic way so as to create a comprehensive picture of challenges, benefits and opportunities involved with DR. The reader will thus be provided with a clear understanding of the complexity deriving from a demand becoming active, as well as with a quantitative assessment of the techno-economic value of the proposed solutions in a Smart Grid context.
Many research contributions have appeared in recent years in the field of DR, both in journals and conference proceedings. However, most publications focus on individual aspects of the problem. A systematic treatment of the issues to be tackled to introduce DR in existing electricity grids, involving the extended value chain in terms of technical and commercial aspects, is still missing. Also, several books have recently been published about Smart Grid, in which there is some mention to DR. However, again while DR is seen as a key pillar for the Smart Grid, there is no dedicated, comprehensive and systematic contribution in this respect.
The recent development of distributed generation technologies is changing the focus of the production of electricity from large centralized power plants to local energy systems scattered over the territory. Under the distributed generation paradigm, the present research scenario emphasises more and more the role of solutions aimed at improving the energy generation efficiency and thus the sustainability of the overall energy sector. In particular, coupling local cogeneration systems to various typologies of chillers and heat pumps allows setting up distributed multi-generation systems for combined production of different energy vectors such as electricity, heat (at different enthalpy levels), cooling power, and so forth. The generation of the final demand energy outputs close to the users enables reducing the losses occurring in the energy chain conversion and distribution, as well as enhancing the overall generation efficiency. This book presents a comprehensive introduction to energy planning and performance assessment of energy systems within the so-called Distributed Multi-Generation (DMG) framework. Typical plant schemes and components are illustrated and modelled, with special focus on applications for trigeneration of electricity, heat and cooling power. A general approach to characterization and planning of multi-generation systems is formulated in terms of the so-called lambda analysis, which extends the classical models related to the heat-to-power cogeneration ratio analysis in cogeneration plants. A unified theoretical framework leading to synthesize different performance assessment techniques is described in details. In particular, different indicators are presented for evaluating the potential energy benefits of distributed multi-generation systems with respect to classical case of separate production and centralized energy systems. Several case study applications are illustrated to exemplify the models presented and to point out some numerical aspects relevant to equipment available on the market. In particular, schemes with different cogeneration prime mover typologies, as well as electric, absorption and engine-driven chillers and heat pumps, are discussed and evaluated. A number of openings towards modelling and evaluation of environmental and economic issues are also provided. The aspects analysed highlight the prominent role of DMG systems towards the development of more sustainable energy scenarios.
Increasing focus on energy affordability and environmental impact is drawing interest towards the potential value of low carbon technology (LCT) interventions in buildings and district energy systems. Relevant interventions may include improvements of the insulation levels and installation of low carbon and renewable generation technologies (e.g., combined heat and power, photovoltaics, heat pumps). These LCT interventions for both electricity and heat supply can, in principle, reduce energy costs and CO2 emissions for final energy consumers. This can be achieved by coupling, and possibly optimising, multiple energy vectors from traditionally independent systems (e.g., electricity, heat and gas). However, this transition to distributed multi-energy systems introduces complex physical and commercial interactions between the different energy vectors. Further, these interactions can be fundamentally different at different aggregation levels (e.g., premises, district, and commercial level). This makes the assessment of business cases for LCT interventions in a multi-energy context a grand challenge, and given the potentially disruptive commercial impact of many such novel technologies, such complexity might result in a barrier to their development. In light of this, this paper proposes a techno-economic framework for the assessment of business cases of LCTs, which systematically models the physical and commercial multi-energy flows at the premises, grid connection point, and commercial levels. This is particularly important given the commonly asymmetrical nature of various energy price components, which can have significant effects on the business cases of LCTs and associated actors (e.g., retailers and energy service companies). The proposed framework is demonstrated through a series of case studies that highlight the value of various LCT interventions and of aggregation, in terms of energy, emission and operational cash flow metrics. The relevance and importance of the framework to developing business cases for various energy system actors, including policy makers and regulators, is discussed, with the final aim of facilitating the uptake of low carbon multi-energy technologies.
An impact of increased variable renewable generation is the need for balancing authorities to procure more ancillary services. While demand response resources are technically capable of providing these services, current experience across the U.S. illustrates they are relatively minor players in most regions. Accessing demand response resources for ancillary services may require a number of changes to policies and common practices at multiple levels. Regional reliability councils must first define ancillary services such that demand response resources may provide them. Once the opportunity exists, balancing authorities define and promulgate rules that set the infrastructure investments and performance attributes of a resource wishing to provide such services. These rules also dictate expected revenue streams which reveal the cost effectiveness of these resources. The regulatory compact between utility and state regulators, along with other statutes and decisions by state policymakers, may impact the interest of demand response program providers to pursue these resources as ancillary service providers. This paper identifies within these broad categories specific market and policy barriers to demand response providing ancillary services in different wholesale and retail environments, with emphasis on smaller customers who must be aggregated through a program provider to meet minimum size requirements for wholesale transactions.
Demand response is increasing in popularity and many utilities are developing demand response programs. However, there exists many challenges to the deployment of demand response. One of the main barriers to widespread rollout is the uncertainty surrounding the value of demand response. In this regard, there is a real and pressing need to evaluate demand response if its full potential is to be realized. This paper presents a comprehensive review of the literature and identifies some of the key barriers to the deployment and the challenges to the evaluation of demand response and provides some recommendations on evaluation methodologies.
In district energy systems powered by Combined Heat and Power (CHP) plants, thermal storage can significantly increase CHP flexibility to respond to real time market signals and therefore improve the business case of such demand response schemes in a Smart Grid environment. However, main challenges remain as to what is the optimal way to control inter-temporal storage operation in the presence of uncertain market prices, and then how to value the investment into storage as flexibility enabler. In this outlook, the aim of this paper is to propose a model for optimal and dynamic control and long term valuation of CHP-thermal storage in the presence of uncertain market prices. The proposed model is formulated as a stochastic control problem and numerically solved through Least Squares Monte Carlo regression analysis, with integrated investment and operational timescale analysis equivalent to real options valuation models encountered in finance. Outputs are represented by clear and interpretable feedback control strategy maps for each hour of the day, thus suitable for real time demand response under uncertainty. Numerical applications to a realistic UK case study with projected market gas and electricity prices exemplify the proposed approach and quantify the robustness of the selected storage solutions.
Demand Side Response (DSR) is recognised for its potential to bring economic benefits to various electricity sector actors, such as energy retailers, Transmission System Operators (TSOs) and Distribution Network Operators (DNOs). However, most DSR is provided by large industrial and commercial consumers, and little research has been directed to the quantification of the value that small (below 100 kW) commercial and residential end-users could accrue by providing DSR services. In particular, suitable models and studies are needed to quantify potential business cases for DSR from small commercial and residential end-users. Such models and studies should consider the technical and physical characteristics of the power system and demand resources, together with the economic conditions of the power market. In addition, the majority of research focuses on provision of energy arbitrage or ancillary services, with very little attention to DSR services for network capacity support. Accordingly, this paper presents comprehensive techno-economic methodologies for the quantification of three capacity-based business cases for DSR from small commercial and residential end-users. Case study results applied to a UK context indicate that, if the appropriate regulatory framework is put in place, services for capacity support to both DNOs and TSOs can result into potentially attractive business cases for DSR from small end-users with minimum impact on their comfort level.
This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer's electricity bill whilst minimizing the daily volume of curtailed energy, and therefore considering the user's comfort level. To avoid shifting a large portion of consumer demand toward the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer's demand goes beyond a prescribed power threshold. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller in minimizing the household's daily electricity bill while {preserving} comfort level, as well as preventing creation of new least-price peaks.
Demand response for residential consumers is making a slow progression, despite its benefits towards various market participants, and the challenges faced by distribution grids concerning the integration of distributed and renewable energy sources, and new demand side applications. While some obstacles are techno-economic in nature, there also exist barriers related to regulation. The objective of this paper is to provide an overview of the current regulatory framework concerning demand response in distribution systems, as well as to identify the main regulatory barriers to implementation for residential consumers. It is found that in some areas, the current regulatory framework is inadequate or incomplete. An analysis of the relevant literature allows to classify these barriers into six categories.
As countries move toward larger shares of renewable electricity, the slow diffusion of active electricity load management should concern energy policy makers and users alike. Active load management can increase capacity factors and thereby reduce the need for new capacity, improve reliability, and lower electricity prices. This paper conceptually and empirically explores barriers to load shift in industry from an end-user perspective. An online survey, based on a taxonomy of barriers developed in the realm of energy efficiency, was carried out among manufacturing sites in mostly Southern Germany. Findings suggest that the most important barriers are risk of disruption of operations, impact on product quality, and uncertainty about cost savings. Of little concern are access to capital, lack of employee skills, and data security. Statistical tests suggest that companies for which electricity has higher strategic value rate financial and regulatory risk higher than smaller ones. Companies with a continuous production process report lower barrier scores than companies using batch or just-in-time production. A principal component analysis clusters the barriers and multivariate analysis with the factor scores confirms the prominence of technical risk as a barrier to load shift. The results provide guidance for policy making and future empirical studies.
Scalability and privacy concerns have created significant interest in decentralized coordination of distributed energy resources (DERs) within microgrids. Previously proposed approaches, however, fail to achieve feasible solutions under flexible demand (FD) and energy storage (ES) participation. After justifying and demonstrating this challenge, this paper develops a novel Lagrangian relaxation-based mechanism achieving feasible, near-optimal solutions in a decentralized fashion, considering both active and reactive power. A two-level iterative algorithm eliminates the infeasibility effect of FD and ES nonstrict convexities, and prevents the creation of new demand peaks and troughs by the concentration of their response at the same low- and high-priced periods. Tradeoffs associated with the design and operation of the mechanism are analyzed, and the value of additional information submission by the DER, in enabling the quantification of an optimality bound of the determined solutions and significant improvements in communication requirements, is assessed. These contributions are supported by case studies on an LV microgrid test system.
It is arguable how much flexibility and efficiency from coupling different energy vectors through available technologies is exploited in current energy systems. In particular, in spite of the growing interest for the multi-energy concept, there are very few models capable of clearly explaining the benefits that can be derived from integration of complementary technologies such as cogeneration, electric heat pumps and thermal storage. In this light, this paper introduces a comprehensive analysis framework and a relevant unified and synthetic Mixed-Integer Linear Programming optimization model suitable for evaluating the techno-economic and environmental characteristics of different Distributed Multi-Generation (DMG) options. Each option's operational performance and flexibility to respond to electricity market signals are analysed in detail and assessed against the needed investment costs in different contexts. Numerical case studies focus on highlighting the flexibility benefits that can be gained in economic terms from multi-energy system integration in district heating (DH) applications. Detailed sensitivity analyses of different DMG configurations also clearly show what economic as well as environmental performance (at both global and local levels) can be expected in current and future scenarios when coupling different energy vectors and complementary technologies in a multi-energy context.
Multi-energy systems (MES) in which operation and planning of electricity networks is optimally framed within a context of interaction with other energy vectors such as heat, cooling and gas, are receiving increasing interest from the point of view of providing flexibility to the power system. In this outlook, MES have the potential to provide real time demand response services by deploying the possibility to internally shift energy vectors between different plant components, thus decreasing the equivalent electricity input from the grid. This could be particularly relevant to provide ancillary services to future systems. On these premises, the aim of this paper is to set out a framework for the techno-economic assessment of integrated provision of energy and ancillary services from MES while supplying local multi-energy demand. The concepts of fuel-to-power arbitrage or equivalently of multi-energy arbitrage, electricity shifting potential, and ancillary services profitability maps are introduced and discussed to synthesize the developed framework. Specific numerical applications are presented to illustrate through case studies the implications of the proposed concepts.
The building sector is the largest energy consumer in the world. Therefore, it is economically, socially, and environmentally significant to reduce the energy consumption of buildings. Achieving substantial energy reduction in buildings may require rethinking the whole processes of design, construction, and operation of a building. This article focuses on the specific issue of advanced control system design for energy efficient buildings.
Many demand response resources are technically capable of providing ancillary services. In some cases, they can provide superior response to generators, as the curtailment of load is typically much faster than ramping thermal and hydropower plants. Analysis and quantification of demand response resources providing ancillary services is necessary to understand the resources' economic value and impact on the power system. Methodologies used to study grid integration of variable generation can be adapted to the study of demand response. In the present work, we describe and implement a methodology to construct detailed temporal and spatial representations of demand response resources and to incorporate those resources into power system models. In addition, the paper outlines ways to evaluate barriers to implementation. We demonstrate how the combination of these three analyses can be used to assess economic value of the realizable potential of demand response for ancillary services.
The need for investment in capital intensive electricity networks is on the rise in many countries. A major advantage of distributed resources is their potential for deferring investments in distribution network capacity. However, utilizing the full benefits of these resources requires addressing several technical, economic and regulatory challenges. A significant barrier pertains to the lack of an efficient market mechanism that enables this concept and also is consistent with business model of distribution companies under an unbundled power sector paradigm. This paper proposes a market-oriented approach termed as “contract for deferral scheme” (CDS). The scheme outlines how an economically efficient portfolio of distributed generation, storage, demand response and energy efficiency can be integrated as network resources to reduce the need for grid capacity and defer demand driven network investments.
The smart grid is conceived of as an electric grid that can deliver electricity in a controlled, smart way from points of generation to active consumers. Demand response (DR), by promoting the interaction and responsiveness of the customers, may offer a broad range of potential benefits on system operation and expansion and on market efficiency. Moreover, by improving the reliability of the power system and, in the long term, lowering peak demand, DR reduces overall plant and capital cost investments and postpones the need for network upgrades. In this paper a survey of DR potentials and benefits in smart grids is presented. Innovative enabling technologies and systems, such as smart meters, energy controllers, communication systems, decisive to facilitate the coordination of efficiency and DR in a smart grid, are described and discussed with reference to real industrial case studies and research projects.
In this paper we propose a framework which categorizes energy efficiency barriers based on the stage at which the barriers exist. Barriers to energy efficiency have been widely studied but to our knowledge, except for a few studies, we found inadequate consideration for barrier–barrier interactions when proposing policy measures for improving energy efficiency. Leveraging systems thinking's power as a problem solver which identifies underlying structure that explains (similar) patterns of behavior in a variety of different situations, we attempted to identify patterns of barriers to adoption of energy efficiency measures in industrial companies. Inspired by systems thinking, the proposed framework has four stages, namely, Motivation, Capability, Implementation and Results, as well as a feedback loop. Using a case study, we show that following the four stages will lead to positive feedback for future energy efficiency implementations. The framework highlights the interconnected nature of the barriers and a need for policymakers to address these barriers in a holistic manner. We argue that the overall effectiveness of energy efficiency policies is only as strong as the weakest link in the four-stage framework. This differs from most prior research that addressed barriers in isolation, where a solution is proposed for each of the barriers without considering the relationship between the barriers. Our framework also offers a way to understand the roles and responsibilities of major stakeholders such as governments and energy service companies (ESCOs) in driving energy efficiency. This allows the assessment and identification of weak links in energy efficiency policies.
Microgrid has caused increasing attention for its high efficiency and low emissions. In this article a microgrid including a wind turbine, pv array and a CHP system consisting of fuel cells and a microturbine is studied and then the modeling of various DERs is conducted and the objective functions and constraints are developed. In the end the generic algorithm is employed to solved the optimal model and an operation scheme is achieved while meeting various constraints on the basis of tariff details, equipment performance, weather conditions and forecasts, load details and forecasts and other necessary information and then the economic costs and environmental impacts are analyzed and a conclusion that the multi-objective model can achieve high environmental benefits and spend as low operation cost as possible. Keywords—Microgrid, optimization,MOGA
Continuous connection of Distributed Energy Resources (DER) technology on a “fit and forget” basis may lead to inefficiently low utilization of generation and network assets. In order to mitigate this effect, a reappraisal of the technical, regulatory, and commercial frameworks that shape decisions on future network design, investment, operation, and pricing are required. The transition of distribution network operation from passive to active would facilitate cost effective integration of DER and an efficient evolution towards a low carbon electricity system. In this context, this paper summarizes the results from a range of quantitative studies on the UK electricity system that have been carried out to assess the benefits of active management of distribution networks.
Only half of the potential for improving U.S. energy efficiency over the next 20 years is likely to be achieved, given current government policies and programs. This large untapped potential to save money, improve environmental quality, and reduce the foreign trade deficit exists because of structural and market barriers that inhibit adoption of cost-effective energy-efficient practices and measures. Structural barriers include distortions in fuel prices, uncertainty about future fuel prices, limited access to capital, government fiscal and regulatory policies, codes and standards, and supply infrastructure limitations. Behavioral barriers include attitudes toward energy efficiency, perceived risk of energy-efficiency investments, information gaps, and misplaced incentives.
Considering the urgency of the need for standards which would allow constitution of heterogeneous computer networks, ISO created a new subcommittee for "Open Systems Interconnection" (ISO/ TC97/SC 16) in 1977. The first priority of subcommittee 16 was to develop an architecture for open systems interconnection which could serve as a framework for the definition of standard protocols. As a result of 18 months of studies and discussions, SC16 adopted a layered architecture comprising seven layers (Physical, Data Link, Network, Transport, Session, Presentation, and Application). In July 1979 the specifications of this architecture, established by SC16, were passed under the name of "OSI Reference Model" to Technical Committee 97 "Data Processing" along with recommendations to start officially, on this basis, a set of protocols standardization projects to cover the most urgent needs. These recommendations were adopted by T.C97 at the end of 1979 as the basis for the following development of standards for Open Systems Interconnectlon within ISO. The OSI Reference Model was also recognized by CCITT Rapporteur's Group on "Layered Model for Public Data Network Services." This paper presents the model of architecture for Open Systems Interconnection developed by SC16. Some indications are also given on the initial set of protocols which will-likely be developed in this OSI Reference Model.