Incentive-based Demand Response Programs Research Articles

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Social welfare maximization with efficient energy management of community microgrid considering customer behavioral response using MDCLPIS

A novel residential demand response (DR) program is proposed to encourage microgrid customers to participate in demand reduction using a house energy consumption scheduler (HECS) to maximize social welfare by optimally reducing the cost of electricity and to receive maximum incentives. In this research, both Price-based and incentive-based DR programs are considered, and the demand-price elasticity approach is implemented to model them. To make the DR model more effective, customer behavioral response is taken into account, by leveling the customers according to their price elasticity. An optimized non-cooperative energy game theory framework and a Nash equilibrium strategy are employed to model customer response under a multi-time of use (TOU) pricing and incentive scheme, with the aim of achieving the best scheduling of home-controlled appliances while preserving customers’ privacy. This approach maximizes social welfare by minimizing the electricity consumption cost for customers while simultaneously saving on generation costs. The negative externality effect on price rate is internalized between the customers of different consumption levels using multi-dynamic consumption-level pricing and incentive scheme (MDCLPIS). The suggested model distinguishes between consumers’ behavior in response to changes in energy prices and their behavior in response to changes in incentives. Finally, numerical and simulation results demonstrate the suitability and effectiveness of the proposed model and its advantages in terms of customer saved cost by up to 21.26%, microgrid saved cost by up to 29.44%, and getting benefits by optimal scheduling of their load.

Integration of mobile power-hydrogen storage systems in distribution-level networks: A fuzzy information gap optimization framework

In recent years, renewable energy sources (RESs) have had unquestionably significant benefits. However, maximum penetration of RESs has always been challenging due to their intermittent nature. To address this challenge, this study advances a framework for the flexibility of the distribution network (DN) by employing transportable electrical storages (TESs) and transportable hydrogen storages (THSs). To do so, the TESs are considered to transfer electrical energy among high-penetrated and high-demand points. In addition, curtailed renewable power can be converted to green hydrogen and transferred to hydrogen refueling stations (HRSs) using the THSs. To further flexibility, this work considers an incentive-based demand response (DR) program and vehicle-to-parking lot (V2PL) capable plug-in electric vehicles (PEVs). Moreover, a binary-based method is developed to carry out the trajectory planning of the TESs and THSs and the Floyd algorithm is exploited to minimize traveling and simulation time. Furthermore, the fuzzy information gap decision theory (IGDT) is proposed to enhance the scheduling robustness against the uncertainty of RESs. The obtained results corroborated the positive impact of the TESs and THSs by declining the operation cost by about 4.8 %. Moreover, the proposed uncertainty management model is quite robust against RESs’ uncertainty by achieving about 37.5 % less operation cost.

Enhancing Peak Shaving through Nonlinear Incentive-Based Demand Response: A Consumer-Centric Utility Optimization Approach

This paper presents a comparative study on the implementation of incentive-based demand response programs in power grid management, comparing the results obtained from linear and nonlinear models. The challenges faced by power grids in balancing supply and demand and managing price spikes during peak periods are addressed, and demand response programs are proposed as an effective strategy. The focus is on implementing incentive-based programs where load serving entities determine the optimal incentive price and demand reduction. A novel approach is presented for simulating consumer behavior based on the price elasticity of demand and consumers’ utility function, incorporating both linear and nonlinear economic models. The calculation of demand reduction aims to maximize consumers’ welfare, while the determination of the optimal incentive price maximizes the profit of the load serving entities. Real data are utilized, and the proposed models are implemented using the mixed-integer nonlinear programming (MINLP) method. The results demonstrate the effectiveness of providing incentives to consumers during peak hours compared to other approaches, with a comparison between the linear and nonlinear models.

Optimal planning and operation of grid-connected PV/CHP/battery energy system considering demand response and electric vehicles for a multi-residential complex building

Ongoing technological innovations and communication infrastructure have rapidly increased the active participation of energy customers and utilization of renewable energy sources and electric vehicles (EVs) as climate change solutions. As such, the demand response program (DRP) has been developed as an effective tool for better interaction mechanisms and balancing supply and demand. This paper proposes a model for optimal planning and operation of an integrated PV/CHP/battery/gas boiler hybrid grid-connected energy system with the purpose of minimizing lifecycle cost. The model considers EV charging stations (private and shared), incentive-based DRP, and net energy metering mechanism. Different alternatives for system configuration are optimized and analyzed in terms of cost, emission, and resiliency factors. A new representative case study of a multi-residential complex building with electrical and thermal loads is depicted to validate the research methodology. The results show that the full utilization of the building roof with maximum PV capacity and adoption of the DRP initiatives for both summer and winter seasons reduces the electricity import from the grid and enhanced the use of the on-site CHP generator and battery packs. The optimal system with 73.9 kW PV, 105 kW CHP, and 53 kW inverter is of superior performance with reduction percentages of 9 %, 10 %, 45 % and 16 % in the system's net present cost, energy cost, annual utility bill, and CO2 emissions, respectively, compared to the base scenario of CHP/gas boiler/grid. Also, the proposed system also gets significant DRP revenue of 10,060$/yr and zero unmet demands and a negligible number of EVs missed charging sessions despite the grid outages and intermittency of solar resources. Overall, this work can provide an understanding and a way forward to those working on iHES development for complex buildings and a valuable benchmark model that is transferable and applicable with relative ease for other regions.

Incentive-based demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer satisfaction on social welfare and profitability

Incentive-based demand response programs (IBDRPs) are voluntary programs that encourage customers (CUs) to cut their electricity usage during peak periods, in return for receiving incentives from the service provider (SP). This research addresses a comprehensive multi-objective modeling approach, based on maximizing the social welfare function of both SPs and CUs involved in the IBDRPs, considering both the economic drivers and the social factors affecting the market actors. The social aspect of the CUs’ participation in the IBDRPs is evaluated with respect to their satisfaction with the intangible benefits, which cannot be measured in monetary terms. To this aim, a Kano model for customer satisfaction is developed to estimate the level of CUs’ satisfaction with participation in the IBDRPs, taking into account the impact of the four attributes of comfort, flexibility, energy security, and environmental protection. The model then analyzes the proposed IBDRP implemented in the Japan Electric Power Exchange (JEPX) market, using real-time wholesale electricity prices and demand loads in the Tokyo residential areas. The results revealed that, the environmental protection attribute could more positively impact the CUs’ satisfaction level; consequently, their welfare is higher with more reduction in electricity from incentive incomes.

Hybrid robust-stochastic multi-objective optimization of combined cooling, heating, hydrogen and power-based microgrids

This paper presents a holistic structure to reach the optimal operation of a combined cooling, heating, hydrogen, and power (CCHHP)-based microgrid (MG). The CCHHP-based MG under study includes a wide variety of energy conversion facilities like power-to-hydrogen (P2HY), hydrogen-to-power (HY2P), electrical chiller (EC), absorption chiller (AC), heat pump (HP), gas boiler (GB), combined heat and power (CHP), wind turbine, and energy storage systems. In the presented structure, a robust-stochastic coordinated optimization method is developed to minimize both daily operation cost and carbon pollution emissions with particular attention to uncertain sources, i.e., electricity market price, renewable power production, and energy demands. Furthermore, the incentive-based demand response (DR) program is used to increase the flexibility of the CCHHP-based MG and manage the behavior of the electrical and heating demands to use the economic opportunities in energy markets. The proposed multi-objective optimization structure is modeled as a mixed-integer linear programming (MILP) for optimal energy management using the ε-constraint method. The developed structure is solved using a CPLEX solver under the GAMS software environment. Several case studies and numerical results are provided to confirm the effectiveness of the proposed structure. Obtained simulation results indicate that utilizing the up-to-date energy conversion facilities, i.e., P2HY, HY2P, and EC, in the optimal coordinated operation with the incentive-based DR program can reduce the system operation cost to 15.44%.

Privacy-Based Demand Response Modeling for Residential Consumers Using Machine Learning with a Cloud–Fog-Based Smart Grid Environment

Demand response modeling in smart grids plays a significant role in analyzing and shaping the load profiles of consumers. This approach is used in order to increase the efficiency of the system and improve the performance of energy management. The use of demand response analysis in determining the load profile enhances the scheduling approach to the user profiles in the residential sector. In accordance with the behavioral pattern of the user’s profile, incentive-based demand response programs can be initiated in the residential sector. In modeling the behavioral pattern of the user’s profile, the machine learning approach is used to analyze the profile patterns. The incentive-based demand response is demonstrated in order to show the importance of maintaining the privacy of residential users, during interactions between demand- and load-profile patterns. In this work, real-time demand response modeling for residential consumers, with incentive schemes, are analyzed. The incentive schemes are proposed in order to show how the privacy of the residential units may be considered, as a result the model is developed with a two-step analysis approach. In the first step, the demand response modeling is performed with the scheduling of appliances on the residential side, by forming hubs in a cloud–fog-based smart grid environment. This process, with an incentive demand response scheme and scheduling of appliances, is performed using an optimal demand response strategy that uses a discounted stochastic game. In the second step, the privacy concerns of the demand response model from the strategy analysis are addressed using a generative adversarial network (GAN) Q-learning model and a cloud computing environment. In this work, the DR strategy model with privacy concerns for residential consumers, along with EV management, is performed in a two-step process and arrives at an optimal strategy. The efficiency and real time analysis proposed in this model are validated with real-time data analysis in simulation studies and with mathematical analysis of the proposed model.

Optimal microgrid programming based on an energy storage system, price-based demand response, and distributed renewable energy resources

Microgrids (MGs) in distribution systems can be operated in far regions at lower investment costs using renewable distributed energy resources (DERs). The present paper introduces a stochastic model for optimal energy-heat programming and the daily storage of an MG. Bi-level stochastic programming is presented for integrated energy-heat scheduling and storage in the presence of an energy storage system (ESS) and demand response (DR) based on social welfare maximization. Out of the incentive-based DR programs, the tender and redemption and the ancillary services market programs were selected and applied to the given model. Besides, the time of use (TOU) -based DR and real-time pricing (RTP) were considered as the price-based demand response (PBDR) programs in optimal programming. The PBDR programs have been included in the objective function using a linear function based on consumer benefits. The proposed bi-level stochastic model was solved using a developed metaheuristic optimization algorithm called the lightning search algorithm (LSA) in the present work. The Latin hypercube sampling (LHS) and KMEANS methods were used to produce and reduce the scenario. The proposed framework was investigated in a 33-bus test model. The obtained simulation results were evaluated from different aspects. The TOU and RTP effects and ESS are shown in obtained numerical analysis by considering the operating cost, total social welfare, and the client's utility function.

Economic dispatch in micro-grids with alternative energy sources and batteries

The problem of economic dispatch in a micro-grid interconnected to the conventional electrical system consists of optimizing the operating cost to satisfy the different operating requirements and the demand for electrical power required by the users. In the development of this proposal, there are challenges caused by the random nature of electricity generation systems based on renewable energy sources, for an adequate operation of the micro-grid, which can be solved through systems Energy Storage System (BESS). This paper addresses the problem of optimal economic dispatch in a microgrid that includes an energy storage system based on a battery bank and interconnected to the main grid through a mathematical programming approach, considering the benefits for customers by reducing their demand of electrical power and taking into account the costs of treatment pollutants produced by conventional energy sources. The generation systems used to satisfy the energy demand of the customers within the interconnected micro-grid are: a) conventional diesel generators, b) wind generation systems, c) photovoltaic systems, d) a hydroelectric turbine, and e) a storage system based on electric batteries. The results of the effectiveness of the proposed mathematical model show that the demand for electrical power to the main grid can be reduced and customers obtain economic benefits by integrating the incentive-based demand response program into the energy management problem when environmental contingency events occur. The results show that when there are peaks in energy demand, the operating costs of the micro-grid increase by up to 380%. In the same way, when there are failures in renewable energy sources, storage systems (BESS) and diesel generators work at 100% of their capacities. The proposal is implemented in Lingo 17.0 software and simulation results show the feasibility of the proposal through the optimization of the operation of the micro-grid.

Elastic flexible unit commitment: A scenario-based power-electric fleet analysis

This paper presents an elastic flexible unit commitment (EFUC) in a coupled power and electric fleet (P-EF) network. In this framework, a master independent system operator (MISO) coordinates the day-ahead unit commitment problem (UCP) while various sources of both networks are integrated. On the power side, battery energy storage (BES) is allocated, and on the electric fleet side, private electric vehicles (PEVs), electric buses (EBs), and electric taxis (ETs) are integrated. The temporalities of PEVs and EBs are managed by PEV to grid (P2G) and bus to grid (B2G) technologies, respectively. Traveling patterns present the spatial impact of PEVs and ETs on day-ahead commitment. In this P-EF network, an incentive-based demand response (IBDR) program is implemented by MISO. A scenario-based technique is applied to generate operation scenarios for coupled uncertainties of load demand and the number of PEVs. The overall mixed integer linear programming (MILP) model is tested on IEEE 6-bus and 24-bus systems which are integrated with traveling patterns of PEVs and ETs. Results confirm that classifying the behavior of various electric vehicles leads to an explicit understanding of each source’s impact on UCP cost and required energy. The results indicate that MISO prioritizes the utilization of spatio-temporal sources (e.g., PEVs) over only stationary temporal ones (e.g., EBs or BES) since both time and location are under control. Also, MISO damps the spike prices by the IBDR program while offering incentives to responsive loads under uncertainty.

A prospect-theoretic game approach to demand response market participation through energy sharing in energy storage systems under uncertainty

The Paris Agreement, which is carried out in a bottom-up manner, is giving great vitality to the transition to low-carbon energy system. Accordingly, as the spread of renewable energy increases, securing flexibility resources is emerging as a keyword in the power system and power market, and many studies on demand response (DR) and energy storage system (ESS) classified as demand-side flexible resources are being conducted. However, most of the studies on DR are on voluntary DR programs that participate through bidding among incentive-based DR or price-based DR programs, and ESS faced difficulties in its distribution due to low economic feasibility. Therefore, in this work, we propose a feasible process for participating in a reliability DR program that guarantees high profitability but involves high uncertainty, and non-cooperative game model is suggested to mitigate the uncertainty through surplus electricity-trading model of ESS. In addition, a subjective decision-making based on prospect theory is proposed to reflect the impact of the uncertainty about the incentive-based program’s engagement. Case study results performed in an environment formed based on actual operation data of the Korean DR market prove that participants’ uncertainty is mitigated when participating in the DR market through power trading of ESS through the proposed model. In addition, a small-scale ESS with an average benefit–cost ratio of 0.78 improves by up to 1.08 (1.04 with a standard scenario).

Consumer Baseline Estimation Analysis for Implementation of Demand Response Programs

ABSTRACT Demand response (DR) is the short-term change of consumers energy usage patterns in response to power utility needs. The utility benefits from DR adoption in terms of better load management, reduction of peak power deficits, energy deficits, enhanced reliability, and improved economic performance. Consumers gain from energy bill savings and incentive payments. Accurately estimating the consumer baseline load (CBL) is critical for evaluating the success of a DR program implementation. Estimation of CBL helps to decide the volume of load curtailment, decide the incentives to be offered, and assess the impacts of consumer participation. The real-time domestic 11 kV feeder data of the 6.3 MVA substation of Goa electricity board, Goa, India is used to analyze the performance of the DR program. For CBL analysis time series average, adjustment, exponential moving average, decomposition, and forecasting techniques are applied. For performance evaluation of DR programs, the Artificial neural network (ANN) based CBL estimation technique is utilized. The ANN-based technique of estimation has better prediction accuracy, lower bias, and reduced variability as compared to other CBL estimation techniques. Consumer response to demand changes is computed using elasticity and consumer benefit function. The ANN-based CBL estimation and demand variation obtained from elasticity values are further used to analyze the technical and financial performance factors, for dynamic price and incentive-based DR program implementation. The research can assist a state power utility that is implementing smart grids in categorizing consumers, establishing a baseline, identifying potential demand reductions, and evaluating the feasibility DR programs.

Adaptive incentive-based demand response with distributed non-compliance assessment

Traditional residential incentive-based demand response (DR) programs use fixed incentive structures that do not incorporate closed-loop feedback to compensate for non-compliance by participants. In practice, such programs may not reliably meet their event goals. To address this challenge, real-time feedback can be used to adaptively modify the participants’ incentives, an approach which has not been proposed before. This paper proposes a flexible monitoring framework to detect potential non-compliance, whereby a second DR event is adaptively scheduled with higher incentives. In this context, constraints are presented to prevent over-compensation and gaming of the DR system by the participants. This novel dual-event design is implemented using a distributed event-stream monitoring framework to preserve scalability and ensure low monitoring costs. The merits of the proposed DR design are demonstrated at a utility-scale for 100,000 residents, while also considering the adoption of residential electric vehicles that are poised to increase the flexibility of the demand in the distribution system.

Optimal energy management of microgrid based wind/PV/diesel with integration of incentive-based demand response program

The combination of demand response as demand side management together with energy management system has become essential to minimize energy cost, to maintain continuous supply of electricity, and to improve the safety of power system operation. This paper studies the optimal energy dispatch of connected microgrid units containing photovoltaic panels, wind turbine generators, diesel generators, and the main grid. The optimal set point of microgrid’s units is determined to satisfy the required load demand for a day-ahead horizon time. As the demand response is an important way of demand side management, this paper proposes as the main contribution the implementation of demand response cost as one of the objective functions to be maximized to view its effect on load demand consumption, on MG energy production and on MG energy cost. The demand response is implemented by using an incentive based demand response program in the optimization model in addition to the fuel cost of diesel generators and the transfer cost of transferable power. The incentive payment offered by utilities is used to motivate consumers to change their energy consumption behavior and thus to reduce their power consumption and maintain the system reliability during on-peak periods. Thus the objective function is formulated to maximize microgrid operator’s demand response benefit, and to minimize both the fuel cost of diesel generators, and the transfer cost of transferable power. For this purpose, the defined objective function is solved by a Hybrid Particle Swarm Optimization with Sine Cosine Acceleration Coefficients (H-PSO-SCAC) algorithm for an optimal energy management system of the connected microgrid. For the simulation tests, different algorithms are examined in order to validate the effectiveness of the H-PSO-SCAC algorithm. The impact of demand response program is analyzed on the load demand consumption, on the microgrid energy production and its influence on the optimized microgrid cost function. The results demonstrate that the implementation of demand response has changed the previous situation that costumers do not participate in the operation of the power system. And it enables microgrid to decrease load consumption, microgrid energy production, as well as energy cost.

A multi-use framework of energy storage systems using reinforcement learning for both price-based and incentive-based demand response programs

This study proposes a multi-use energy storage system (ESS) framework to participate in both price-based and incentive-based demand response programs with reinforcement learning (RL) on the demand side. We focused on industrial customers, to provide them the opportunity to obtain additional profits through market participation in addition to managing their load. Since industrial customers pay their electricity bills according to the time of use tariff structure, they can benefit if they can shift their electricity usage from high-price hours to low-price hours. Furthermore, they are able obtain additional incentives by fulfilling a dispatch signal from the system operator by using the ESS. To model the multi-use ESS by industrial users, we used the RL framework to make customer decisions. The RL approach uses a control action policy by interacting with an environment with no prior knowledge. For this, we formulated the ESS operation as a Markov decision process so that the environmental information obtained by the customers provides RL, which takes optimal actions for the current environment considering customer benefits. We developed several RL agents to identify an acceptable control agent. We utilized the actual industrial load profile in South Korea to train the RL agents. The experimental results demonstrated that the proposed framework can make near-optimal decisions for using ESS in multiple ways.

Analysis of Price and Incentive Based Demand Response programs on Unit Commitment using Particle Swarm Optimization

The increase in power demand by various infrastructural development activities and industrial automations in recent years have made a vital effect with respect to the load demand. To effectively manage the load demand, several Load Management (LM) techniques has been adopted in all energy policy decisions. In the de-regulated power system, the Demand Side Management (DSM) owing to its advantages at economic environments are regarded as remarkable choice and has been extended to incorporate Demand Response Programs (DRPs) in the load management techniques. In this paper, a responsive load economic model is developed. This model is based on the two factors such as price elasticity of demand and welfare function of customers. A Demand Response (DR) based Unit Commitment (DRUC) problem is studied to execute the economic analysis of DRPs. The main idea behind the DRUC is to maximize the benefit of both customers and utilities. The Particle Swarm Optimization (PSO) technique has been used to resolve the unit commitment problem with and without DRPs. The optimization outcomes are also compared with the conventional methods. To verify the efficacy of the DRUC model, the conventional ten-unit test system is considered. The simulated result shows that Critical Peak Pricing (CPP) and Direct Load Control (DLC) gives the best peak reduction and Time of Use (TOU) gives the best total cost reduction among all the DRPs and the other conventional methods.

Consumer preferences for the design of a demand response quota scheme – Results of a choice experiment in Germany

Demand response (DR) programs are increasingly discussed as policy options to facilitate an efficient energy transition. More recently, quota schemes, a novel type of incentive-based DR program that aims to restrict electricity consumption of some household appliances at certain times, have received considerable attention as a tool for preventing local grid congestion. However, little is known about the preferences of household consumers regarding the design of DR programs in general and quota schemes in particular. We examined the preferences of 1034 German consumers using data from a choice experiment. Our model results show that respondents’ choices for quota scheme designs are mainly driven by the time period during which consumption is constrained, followed by the financial compensation. That said, the order of importance reverses if consumers are free to choose whether to participate or not, while the frequency and duration of DR measures remain unimportant. This shift in preferences suggests that preferences for certain DR programs may not necessarily translate into willingness to participate. Sociodemographic characteristics explain these preferences only to a limited extent, with female and older persons and persons currently purchasing green electricity showing a slightly higher willingness to participate. We discuss policy implications arising from these findings.

Coordinated control of Incentive-Based Demand Response Program and BESS for frequency regulation in low inertia isolated grid

With the increase in penetration of inverter-based renewable energy resources (RES) to meet the increasing demand, frequency regulation is a major concern in low inertia isolated microgrids. Conventionally, a battery energy storage system (BESS) regulates the frequency in coordination with RES. Using only BESS for frequency regulation may not be sufficient during high penetration of RES. Therefore, maintaining the BESS state of charge (SOC) at a nominal value for up/down frequency regulation services becomes daunting. Customers’ participation through the Demand Response (DR) programs is investigated to provide frequency regulation services when the existing BESS and other resources are insufficient for regulation services. The coordination between the Incentive-Based Demand Response (IBDR) program and BESS is used to provide frequency regulation services and maintain the nominal SOC of the battery. For the IBDR program, the power consumption of commercial buildings’ heating ventilating and air-conditioning (HVAC) systems are controlled by changing air handling units’ fan speed (AHUs). The speed of the fan in AHUs is tracked to regulate the frequency. A decentralized control scheme with a simple PI and PD controller is proposed. The simulated results show that smooth frequency regulation is achieved through coordinated control of HVAC loads and BESS.

A stochastic tri-layer optimization framework for day-ahead scheduling of microgrids using cooperative game theory approach in the presence of electric vehicles

This study provides a tri-layer optimization framework in which the microgrid strategy for day-ahead market participation is determined by considering the uncertainties of load, RESs and EVs. Scenario-based method is utilized to deal with uncertainties. Besides, distribution feeder reconfiguration (DFR) and an incentive-based demand response (DR) program are used to enhance scheduling flexibility. In the first layer of the proposed model, the buy/sell bids of the microgrids are determined and sent to the pool market. In the second layer, the market clearing price (MCP) is determined according to the buy/sell bids of microgrids, and in the third layer, the microgrids are scheduled by a cooperative game theory approach. The proposed model is implemented on a 118-bus distribution system consisting of 4 microgrids and the results show that the dynamic topology improves the scheduling flexibility and thus reduces the total operating cost by 9.2%. The simulation results also show that EVs participation in scheduling leads to a reduction in the MCP during peak hours. Finally, the results illustrate that considering energy storage systems (EESs) and DR program leads to a reduction in the MCP and thus a 9.33% reduction in the total operating cost.