Optimal Dispatch Model for Demand Response Aggregators

In this paper, we model the Demand Response eXchange (DRX) as an entity that facilitates the trading of demand response (DR) in the existing bulk power market through DR aggregators (DRA). DR as a service is used by the independent system operator (ISO) only when the market is settled inefficiently. A simple threshold on locational marginal price (LMP) cannot be set as the market clearing price depends on various factors, such as the transmission network availability, weather, and available resources. To detect inefficiency in the day-ahead market, we use statistical pattern recognition and compare among the various available techniques to integrate the DRX into a fully deregulated day-ahead market clearing method. We use support vector machines (SVM) to detect market inefficiencies during market clearing using real-data from the PJM ISO, and validate on the IEEE 24-bus system. We show that the DRX can be integrated into the existing bulk power market, with the ISO using pattern recognition techniques to detect market inefficiencies and trigger the DRX during such hours.

The demand response (DR) program is a promising way to increase the ability to balance both supply and demand, optimizing the economic efficiency of the overall system. This study focuses on the DR participation strategy in terms of aggregators who offer appropriate DR programs to customers with flexible loads. DR aggregators engage in the electricity market according to customer behavior and must make decisions that increase the profits of both DR aggregators and customers. Customers use the DR program model, which sends its demand reduction capabilities to a DR aggregator that bids aggregate demand reduction to the electricity market. DR aggregators not only determine the optimal rate of incentives to present to the customers but can also serve customers and formulate an optimal energy storage system (ESS) operation to reduce their demands. This study formalized the problem as a Markov decision process (MDP) and used the reinforcement learning (RL) framework. In the RL framework, the DR aggregator and each customer are allocated to each agent, and the agents interact with the environment and are trained to make an optimal decision. The proposed method was validated using actual industrial and commercial customer demand profiles and market price profiles in South Korea. Simulation results demonstrated that the proposed method could optimize decisions from the perspective of the DR aggregator.

We design an optimal contract between a demand response aggregator (DRA) and power grid customers for incentive-based demand response. We consider a setting in which the customers are asked to reduce their electricity consumption by the DRA and they are compensated for this demand curtailment. However, given that the DRA must supply every customer with as much power as she desires, a strategic customer can temporarily increase her base load in order to report a larger reduction as part of the demand response event. The DRA wishes to incentivize the customers both to make costly effort to reduce load and to not falsify the reported load reduction. We model this problem as a contract design problem and present a solution. The proposed contract consists of two parts: a part that depends on (the possibly inflated) load reduction as measured by the DRA and another that provides a share of the profit that accrues to the DRA through the demand response event to the customers. Since this profit accrues due to the total load reduction because of the actions taken by all the customers, the interaction among the customers also needs to be carefully included in the contract design. The contract design and its properties are presented and illustrated through examples.

The penetration of wind generation in distribution networks is steadily increasing and may provide ample business opportunities for aggregators. While, residential demand response (DR) has proven to be a feasible economic tool in smart grids, the quantitative evaluation of various aggregator-based DR services is required to tap the maximum benefits from the DR flexibility. This paper provides an overview of benefits of different DR aggregator services such as minimization of energy cost and wind curtailment given the scenario of large wind generation hosted by distribution network. An optimization model is formulated from aggregator perspective to manage the population of heating, ventilation and air-conditioning (HVAC) loads for optimizing the benefits of demand response in energy market and for wind integration services. The application of the model suggests that maximum benefits are pocketed by the aggregator when DR services are jointly optimized.

Due to the limited predictability and associated uncertainty of renewable energy resources, renewable-based electricity systems are confronted with instability problems. In such power systems, implementation of Demand Response (DR) programs not only can improve the system stability but also enhances market efficiency and system reliability. By implementing cloud-based engineering systems the utilization of DR will be increased and consequently DR will play a more crucial role in the future. Therefore, DR aggregators can efficiently take part in energy, balancing and ancillary services markets. In this paper, a model has been developed to optimize the behavior of a DR aggregator to simultaneously participate in the mentioned markets. To this end, the DR aggregator optimizes its offering/bidding strategies based on the contracts with its customers. In the proposed model, uncertainties of renewable energy resources and the prices of electricity markets are considered. Numerical studies show the effectiveness of the proposed model.

Demand response for variable renewable energy integration: A proposed approach and its impacts

In the past two decades, interest in demand response (DR) schemes has grown exponentially. The need for DR has been driven by sustainability (environmental and socioeconomic) and cost-efficiency. The main premise of DR is to influence the timing and magnitude of consumption to match energy supply by sharing the benefits with consumers, ultimately aiming to optimize generation cost. As such, the first and primary enabler to DR was the establishment of contemporary electricity markets. Increased proliferation of Distributed Energy Resources (DER) and microgeneration further motivated the participation of consumers as active players in the market, popularizing DR and the wider category of Demand-Side Management (DSM) programs. Smart Grids (SG) have been an enabler to modern DR schemes, with smart metering data providing input to the underlying optimization and forecasting tools. The more recent emergence of the Internet of Energy (IoE), seen as the evolution of SG, is driven by increased Internet of Things (IoT)-enabling and high penetration of scalable and distributed energy resources. In this IoE paradigm being a fully decentralized network of energy prosumers, DR will continue to be a vital aspect of the grid in future Transactive Energy (TE) schemes, aiming for a more user-centered, energy-efficient, cost-saving, energy management approach. This paper investigates original motives and identifies the first mentions of DR in the legislative and scientific literature. Afterwards, the evolution of DR is tracked over the past four decades, attempting to study the co-influence of legislation and research by performing a thorough statistical analysis of research trends on the IEEE Xplore digital library. Finally, conclusions are made as to the current state of DR and future prospects of DR are discussed.

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.

In a re-structured environment, demand response (DR) programs are presented in a new style. The demand response program consists of demand-side management methods which answers to electricity price variations. With advent of electricity markets, the demand-side management programs was introduced into two categories: (1) energy efficiency program, (2) demand response program. This paper studies engagement of participants load in an electricity market having a grid with large and small loads interested to participate in the market, energy storage systems, micro-grids, and distributed generations. In the proposed scheme, it is supposed to have an aggregator which sends demand-side preferences including load curtailment, load shifting, onsite generation, and energy storage systems along with proposed value and prices and all respective system constraints to the independent system operator (ISO). In fact, load aggregators submit aggregated DR offers to the ISO in order to optimize final decisions on aggregators’ DR contributions in wholesale market. The main goal of this paper is to solve the operation problem considering demand-side, system, and pollution and reliability constraints. Compared to other methods, the results indicate that the explicit modeling of customer DR would provide ISOs with more flexible options for scheduling the available energy resources in day-ahead energy markets.

Demand Response (DR) is an important tool for Independent System Operators (ISO) for reliable operation of electricity markets, and there has been considerable interest from load-side market participants to offer their services for DR provisions. In this environment, the ISO needs to develop effective mathematical models for procurement of DR so as to maximize its benefits, such as reducing the peak demand, regulating electricity price shocks, and mitigating market power. In this paper, a new mathematical model for a Locational Marginal Price based, loss included, day-ahead, co-optimized, energy and spinning reserve market including DR provisions, is proposed. The proposed model includes a new proposition for load-side participants to submit price responsive demand (PRD) and load curtailment based DR bids simultaneously, in a unified market framework. Case studies considering DR providers participating in energy market only, in both energy and spinning reserve markets, and in spinning reserve market only, are presented, which examine the impact on system operation, market prices, and DR provider’s benefits. The proposed model is tested on the IEEE Reliability Test System to demonstrate its functionalities and the results clearly justify the merits of simultaneous consideration of PRD bids and load curtailment based DR offers in a co-optimized energy and spinning reserve market.

For the uncertainty of wind power, PV and load in virtual power plant (VPP), a two-stage optimal dispatching model considering the participation of demand-side response resources in VPP is developed from two stages: day-ahead and real-time, taking into account the forecast error and customer-side demand response resources. In the day-ahead stage, a day-ahead VPP optimal economic dispatch model is developed using price-based demand response and incentive-based demand response to optimize customer load curves with VPP operating cost as the target. In the real time stage, taking into account the regulation characteristics of the excitation type demand response, the real time power deviation is smoothed together with the internal generator set of VPP to realize the correction of the power output of the equipment before the day. Both phases of the model are modeled on MATLAB software and solved by the CPLEX optimization solver. The simulation results show that the introduction of price-based demand response and incentive-based demand response together can optimize the load profile of users better and reduce the operating cost of VPP; the two-stage optimal scheduling can smooth out the random fluctuations on both sides of the source load in the real-time phase and realize the economic and stable operation of VPP.

Power systems have been undergoing radical changes in recent years, and their planning and operation will be surely undertaken according to the Smart Grid (SG) vision in the near future. The SG initiatives aim at introducing new technologies and services in power systems, to make the electrical networks more reliable, efficient, secure and environmentally-friendly. In particular, it is expected that communication technologies, computational intelligence and distributed energy sources will be widely used for the whole power system in an integrated fashion. In particular, nowadays, unprecedented challenges like as stringent regulations, environmental concerns, growing demand for high quality, reliable electricity and rising customer expectations are forcing utilities to rethink about electricity generation and delivery from the bottom up. Moreover, the availability of low cost computing and telecommunications technologies, new generation options, and scalable, modular automation systems push utilities to be dynamic, innovative and ambitious enough to take advantage of them. Driven by the dynamics of the new energy environment, leading utilities, technology vendors and government organizations have created a vision of the next generation of energy delivery systems: the Smart Grid. Operational changes of the grid, caused by restructuring of the electric utility industry and electricity storage technology advancements, have created an opportunity for storage systems to provide unique services to the evolving grid. Especially Battery Energy Storage Systems (BESSs), thanks to the large number and variety of services they can provide, are powerful tools for the solution of some challenges that future grids will face. This consideration makes BESSs critical components of the future grids. The BESS can be applied for different services into the different levels of power system chain to satisfy technical challenges and provide financial benefits. In the context of the application of BESSs in SGs, there are two main problems that need to be addressed in a way that exploits the BESS potential, that are linked to their operation and sizing. This thesis focuses on both these aspects, proposing new strategies that allow optimizing the BESS adoption. When dealing with BESSs, sizing and operation are strictly linked. The correct sizing of a BESS, in fact, needs to take into account its operation which in turn will be effected with the aim of optimizing the whole system where it is included. In the first part of this research study, advanced optimal operating strategies were proposed for BESSs by considering both the distribution system operator perspective and the end user. Thus, the proposed operating strategies were performed with the aim of (i) leveling the active power requested by the loads connected to a distribution system (distribution system operator service), (ii) reducing the electricity costs sustained by an end-use costumer that provides demand response (DR) (end user service) and (iii) scheduling a microgrid (µG) with DR resources such as Plug-in Electric Vehicles (PEVs) and Data Centers (DCs) (both the two section service). The proposed strategies also satisfied technical constraints of BESSs and other components of the µG. The second part of the thesis presented the optimal sizing of BESSs aimed at maximizing the benefits related to their use. In the thesis, the sizing, which is performed by considering the end user point of view with reference to both the industrial and residential customers, is effected by adopting both deterministic and probabilistic approaches. With reference to the deterministic approach, a simple and quick closed form procedure for the sizing of BESSs in residential and industrial applications was proposed. In case of probabilistic approach, the case of a BESS installed in an industrial facility was considered and the sizing was performed based on the decision theory. Technical improvements and economic benefits of optimal operation and optimal sizing of BESSs in SG are demonstrated by the obtained results which are reported in the numerical applications. More specifically, it was clearly determined that BESSs can offer technical supports into the distribution operator section of the grid in terms of load management and security challenges. Moreover optimal integration of BESSs into the grid was also appealing for end users thanks to valuable amounts of electricity bill cost reduction. Regarding the original contribution of the thesis, the following considerations can be done. With reference to the load leveling service, an innovative two-step procedure (day-ahead scheduling and very short time predictive control) was proposed which optimally controls a BESS connected to a distribution substation in order to perform load leveling. In case of DR, a proper control of the BESS was proposed in order to perform DR under different price schemes, such as Real Time Pricing (RTP) and Time of Use (TOU) without modifying the daily work cycle of the industrial loads. The control procedure allows achieving contemporaneously two important goals that are the reduction of the bill costs and the prolonging the battery's lifetime so further reducing the costs sustained by the customer. With reference to the scheduling of microgrids, the original contribution of the thesis is focused on the proposal of optimization strategies aimed at managing and coordinating, simultaneously, batteries on board of vehicles or equipping data centers' Uninterruptable Power Supply (UPS) and Distributed Generation (DG) units. Also comparisons among different single-objective based strategies are made in order to highlight the most convenient. With reference to the sizing based on deterministic approach, unlike the other relating literature, the innovative contribution is that the closed form procedure takes into account both the technical constraints of the battery and contractual agreements between the customer and the utility. Moreover, in the economical analysis performed for the sizing, which is applied with reference to both residential and small industrial customers and is based on actual TOU tariffs, a wide sensitivity analysis to consider different perspectives in terms of life span and future costs was performed. Some aspects that affect the profitability of the battery, such as technological limitations (e.g. the battery and converter efficiency), economic barriers (e.g. capital cost and the rate of change of the cost) and variation of the load profile along the years were deeply analyzed. In case of sizing based on probabilistic approach, the original contributions of the thesis are mainly referred to the proposal of a new method that uses a decision theory-based process to obtain the best sizing alternative considering the various uncertainties affecting the sizing procedure. The thesis is organized in three chapters which are dealing with integration of BESSs in SGs. The first chapter reports basic concepts and characteristics of BESSs, fundamental components and features of SGs and different services that BESSs can provide. The optimal operation strategies of BESS are considered in second chapter which includes their problem formulation, solving procedures and results. The third chapter deals with the optimal sizing problem of BESSs for which the problem formulation, solving procedures and results are reported. Finally, the conclusions are presented in the last part of thesis.

Changes in consumer behavior and real-time price fluctuations can significantly influence the profitability of electricity service providers, particularly Demand Response Aggregators (DRAs). During peak load periods and real-time market price surges, DRAs must balance profit maximization with grid stability by strategically participating in the market and coordinating with distribution network operators. This is achieved through incentive-based demand response programs that encourage residential and commercial consumers to reduce electricity consumption in exchange for financial incentives provided by the network operator. This paper proposes an optimal economic model for incentive-based demand response, aiming to determine the optimal incentive pricing and achievable demand reduction while maximizing aggregator profit. The model incorporates the price elasticity of demand and customer utility functions to establish an optimized framework for consumer engagement. Under this framework, the optimal load reduction is determined to enhance consumer welfare, followed by the formulation of an aggregator profit maximization strategy to derive the optimal incentive pricing. The proposed model is validated using real-world data and implemented in the GAMS software. The results demonstrate that the approach effectively increases the profitability of both aggregators and consumers while significantly reducing peak-hour electricity consumption and mitigating price spikes. Furthermore, the findings highlight the crucial role of the proposed method in improving grid stability during peak demand periods, making it a valuable tool for sustainable energy management.

In this paper, a three-stage stochastic bi-level optimization framework is presented for optimal participation of wind power producers (WPPs) in day-ahead (DA), intraday, and balancing markets. In this framework, to leverage demand response (DR) services, a peer-to-peer (P2P) energy trading platform is implemented that allows local load aggregators (LAs) to contribute to the intraday markets improving both LAs’ and WPP’s benefits. Participating in the intraday DR exchange (IDRX) market enables WPP to purchase DR services from LAs, to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. A Stackelberg game for the bi-level decision-making model captures the conflict of interests between the WPP and LAs, in which, the upper level seeks to maximize WPP profit, while the lower level aims to maximize LAs’ economic surplus. The bi-level model is converted into its equivalent single-level mixed-integer quadratic problem (MIQP) employing the Karush-Kuhn-Tucker (KKT) conditions and strong duality theorem. Simulation results show that participation of the WPP in the IDRX market and employing spinning reserve and DR services for compensating the uncertainties are greatly dependent on its risk preferences and increase its expected profit in all conditions, significantly.

Implementing an applicable demand response (DR) program enables the complete demand-side potentials and ensures a secure, more economic and greener operation of the power systems with the integration of renewable energy. Therefore, the present paper proposes a stochastic security-constraint scheduling approach for optimum operation of both supply and demand sides via well-designed pricing and incentive schemes. The DR programs are time-of-use and emergency DR programs. The study addresses the Independent System Operator (ISO)'s viewpoint, and it aims at finding the optimal DR strategy (from a set of DR programs) in a way that an efficient electricity market is obtained, ensuring the security and environmental constraints. To this end, a security constraint unit commitment (SCUC) problem considering DR and renewable energy resources is proposed. Different indices are considered through a multi-objective problem for evaluating the efficiency of the market, security of the system, reliability and air pollution. These indices include market prices, social welfare, load factor (peak-to-valley proportion), air pollution, and power security, among others. In order to find the best DR strategy, a multi-objective problem is solved to consider all the mentioned indices.