Presence Of Demand Response Program Research Articles

A pool-based electricity retail markets integrating renewable energy sources and electric vehicles in the presence of demand response program and conditional value at risk to enhance energy security

In restructured power systems, energy security has become a critical concern, driven by changes in laws and regulations and underscored by a defensive approach implemented by the independent system operator. This alignment highlights a strong interdependence between energy security, energy supply, and the economic aspects of energy management. The paper explores how consumers energy supply in restructured systems can be managed through Demand-Side Management (DSM) and Demand Response (DR) programs, considering economic efficiency and risk in scenarios that include Renewable Energy Sources (RES), suitable storage systems, and electric vehicles (EVs). An energy security index is introduced to evaluate the impact of DR programs on both electrical and thermal energy, considering the economic risks involved. A hybrid pool-based electricity retail market approach is utilized to optimize energy security, supply, and consumption. The significant roles of RES, storage systems, and EVs are highlighted in this context. Economic and environmental optimization is achieved using the Conditional Value at Risk (CVaR) method, facilitating strategic decision-making under varied risk conditions. The energy supply security is further analyzed in the presence of electricity market retailers, exploring different pricing strategies. A Mixed-Integer Programming (MIP) model, implemented in GAMS software with the CONOPT solver, is used to examine pricing methods such as Time-of-Use (TOU) and real-time pricing. Findings reveal that DR programs lead to substantial increases in retailer profit and energy supply security value, with real-time pricing showing a significant 23.5 % increase in energy supply security value and a 24.5 % increase in retailer profit compared to fixed tariff pricing.

A robust optimization model for multiple electricity retailers based on electricity trading in the presence of demand response program

This paper focuses on the short-term cooperation between the electricity retailers at distribution network level and proposes an electricity trading based cooperative mechanism to manage the cooperation between retailers. Under the cooperative mechanism, the objective of the retailers who are engaged in cooperation is to maximize their overall profit through appropriate electricity trading. The profit maximization problem for the retailers is formulated as a robust optimization model and solved via heuristic algorithms. The profit increment brought by electricity trading is allocated according to the proposed profit allocation method. The simulation results demonstrate that the proposed mechanism can effectively increase the overall profit of the retailers. Compared with the non-cooperative case, the cooperative case can increase the overall profit of 427.34 $ at best. Moreover, the results demonstrate that the number of the retailers involved in electricity trading has a positive impact on overall profit increment. The results also reveal that each retailer can achieve profit growth even under the most unfavorable conditions by adopting the profit allocation method.

Optimal integration of CCHP with electric Vehicle parking lots in energy hub

Nowadays, due to environmental problems and the lack of fossil fuels, the use of Electric Vehicles (EVs) is extended. These systems need optimal planning and management, like all energy tools. With another look at electrical generation systems, the combined energy production units have also increased dramatically, with only one kind of fuel, it is possible to produce any kind of energy such as electrical, thermal, and cooling energies at the same time. This production type is called the energy hub. The main goal of creating an energy hub is to meet several types of demand at the same time and to increase the flexibility of the system. The Combined Cooling, Heat, and Power (CCHP) unit, which has a higher efficiency ratio to traditional systems, could be referred to as the main component of the energy hub. To reduce the losses of these systems, their planning could be implemented in coordination with the EV parking lot. The energy hub system could provide the mentioned conditions in the presence of renewable energy sources and energy storage units in a coordinated and optimal way. In this paper, the presence of an EV parking lot in an energy hub is investigated that includes a CCHP, a photovoltaic unit in the presence of demand response programs, and its effects on optimizing power consumption to reduce cost. The demand response programs are used to reduce the operating costs of the energy hub. Moreover, the uncertainties of EV consist of entry and exit times and charge level, solar irradiation, and loads value are applied in the model. Finally, the cost of risk by the conditional value–at–risk method is investigated. The simulations are done by determining 3 various case studies for better investigation of the proposed model. The results show that the proposed solution method of the demand response program could decrease the total cost of the energy hub by about 1.90 % in both summer and winter, under risk-averse conditions.

Providing an optimal demand response program through placement of automatic switches and energy storage systems to improve the reliability of power distribution networks

AbstractCreating highly reliable distribution networks is becoming increasingly vital in today's society. In a similar vein, utilities have issues in properly planning and developing distribution networks, both to reduce their imposed costs and to meet the demands of their consumers. In the presence of Demand Response Program (DRP), this research provides a coordinated architecture that considers automated switches and Energy Storage Units (ESUs) placement with the uncertainty of repair time to enhance the reliability of power distribution network. The suggested objective for optimal placement of automatic switches, ESUs and DRPs is to minimize a combination of reliability index (SAIDI) and Total Cost of system (TC). Customer interruption cost, energy not supplied cost, automated switch investment, ESU investment, ESU participation cost, and DRP cost are all considered as parts of the total cost. The proposed method is used in four different cases on a common reliability test system, followed by numerous sensitivity studies, in order to illustrate applicability and efficiency of suggested method.

Robust multi-objective optimization for the Iranian electricity market considering green hydrogen and analyzing the performance of different demand response programs

Using renewable energy sources (RES) and green hydrogen has increased dramatically as one of the best solutions to global environmental issues. Applying demand response programs (DRPs) in this context could enhance the system’s efficiency. Evaluating different DRPs’ performances and assessing economic impacts on different parts of the electricity market is essential. The inherent uncertainty of RES and prices is inevitable in electricity markets. As a result of the lack of information, it is crucial to mitigate the risks as much as possible, such as risks related to changes in demand, unit outages, or other traders’ bid strategies. This research introduces a robust multi-objective optimization method to reach the most confident plan for the retailer based on uncertainty in RES and price. The integration of different DRPs is assessed according to the cost to retailers and benefits for consumers using a multi-objective model to survey the impacts of different parts’ decisions on each other. The trade-off among DRPs is considered in this model, and they are traded using a new model to illustrate the daily effect of these programs in monthly operations. This paper uses hydrogen storage (HS) integrated with PV as a distributed energy resource. As the Iranian electricity market has just been established, this research proposes a framework for decision-making in new electricity markets to join future smart energy systems. The mid-term pricing evaluates the system’s performance for more accurate monthly results. Also, the operation cost of the hydrogen storage is modeled to assess its performance in non-robust and robust scheduling. Mixed-integer linear programming (MILP) has been used to model this problem in GAMS. A developed linearizing method is considered with a controllable amount of errors to reduce the volume and time of the computation. Finally, the cost of consumers in non-robust and robust market planning in the presence of DRPs is reduced by 8.77 % and 9.66 %, respectively, and HS has a compelling performance in peak-shaving and load-shifting.

Impact of diverse penetration levels of thermal units on a hybrid microgrid energy management considering the time of use and function priority

This paper investigates the eco-emission analysis of a hybrid microgrid by assuming different penetration levels of thermal resources and considering storage devices in the presence of demand response programs. The dual-objective scheduling is done for 24 h (i.e., 4*6-time intervals). The total cost and contamination of the hybrid microgrid are considered as objectives. The time of use program (TOU) is considered on both electrical and thermal loads to determine the effect of demand response programs (DRP) on the planning. The impact of objective function priority is also applied to overcome decision obstacles. The hybrid microgrid consists of electrical and thermal loads; on the generation side, it has a diverse set of thermal resources like combined heat and power (CHP) unit, boiler, furnace, solar water heater, air to water heat pump, wind turbine, photovoltaic systems, micro-turbine. The problem is modeled as a mixed-integer linear programming (MILP) model in General algebraic modeling system (GAMS). Epsilon-constraints method and the fuzzy method are used for dealing with multiple objectives. The results show that thermal resources availability and management significantly impact the objectives; therefore, the cost and pollution are reduced from $295.6 and 2173 kg to $ 253.5 and 1929.7 kg in a nominal scenario. Also, remarkable results are obtained by using demand response programs and function priority in electrical and thermal loads.

A Smart Charging Method for Optimum Electric Vehicles Integration in the Distribution System in Presence of Demand Response Program

A Smart Charging Method for Optimum Electric Vehicles Integration in the Distribution System in Presence of Demand Response Program

Economic and emission analysis of running emergency generators in the presence of demand response programs

Industrial plants in Iran, similar to many other developing countries, use diesel or natural gas generators mainly as backup power during a grid outage. Depending on the electricity network configuration, these emergency generators might be able to deliver electricity to the grid and thereby contribute to demand response (DR) programs. Hence, Iran's Ministry of Energy (MOE) has policies and incentives to promote the use of emergency generators during peak hours. This paper investigates the economic and carbon dioxide emission implications of such DR policies by the MOE. Results show that the current policy promotes diesel generators that are economically beneficial to the companies but are more carbon intensive than the natural gas generators. To mitigate the climate change with a minimum impact on the economic interest of the companies, the future DR policies can be amended. The revised incentive structure proposed by this study is simple but effective and ensures that the lower carbon emitting natural gas generators would become more economically attractive than diesel generators. Following the proposed approach to set the incentives, the natural gas generators would be more beneficial according to the MOE policy in 2019 if the difference between the diesel incentive and natural gas incentive was less than 711 Rial.

Robust management and optimization strategy of energy hub based on uncertainties probability modelling in the presence of demand response programs

The energy hub is considered one of the most important parts of the multi-carrier energy distribution systems. One of the major challenges facing the energy hub idea is the optimal distribution and management of energy based on renewable and non-renewable power generation resources in the presence of demand response programs and uncertainties. This study aims to present an optimal management and distribution model of energy based on uncertainties of probability modelling of renewable energy resources, uncertainties of electrical loads (electric vehicles (EVs)), operating and maintenance cost, cost modelling of greenhouse gas emissions, and electric and thermal demand response programs. Therefore, the proposed model is presented based on a robust optimization model and mixed-integer linear programming. Uncertainties in this research are divided into two categories; technical and economic. The technical part includes the uncertainties caused by renewable energy resources and EVs, simulated based on the Monte Carlo method and their probability distribution function. The economic part includes modelling the uncertainty caused by the price of electricity. The operation model and the objective function of the optimization problem are modelled using the demand response models based on peak shaving and load shifting techniques.

Decentralized blockchain-based peer-to-peer energy-backed token trading for active prosumers

This paper designs and models a fully decentralized peer-to-peer (P2P) energy token market for small-scale prosumers using blockchain technology in a smart grid environment in the presence of the demand response (DR) program and demurrage mechanism. As the market players, prosumers in the local distribution network are considered in two groups, producers (sellers) and consumers (buyers). Using smart contracts, all sellers and buyers in the proposed market can engage in bilateral energy token transactions (P2P) with each other under an agreed price and with the retail market at a certain price. Furthermore, the local consumers can participate in price-based DR programs and shift their consuming load to the periods with high local generation. Demurrage mechanism is applied to avoid energy token accumulation and enhance attraction for local transactions. With demurrage in place, the redemption value of energy-backed tokens reduces with time. A fully decentralized approach called the primal-dual sub-gradient method is developed to clear this fully decentralized energy market in the presence of DR programs and demurrage. The proposed market-clearing scheme guarantees the global and feasible solution without requiring the players' private information. Numerical studies demonstrated the feasibility and effectiveness of the proposed energy token market and the decentralized approach for its clearing.

A smart discrete charging method for optimum electric vehicles integration in the distribution system in presence of demand response program

Electric vehicle charging is one of the common techniques for techno-economic energy management in the distribution system, which, if implemented properly, brings several benefits such as the peak load shaving, total costs reduction, losses minimization, etc. Unlike traditional charging methods, it is not necessary to completely charge electric vehicles. Instead, each vehicle can be charged smartly just based on its demand for its next trips. In this way, charging takes less time and the total consumption is balanced without any discomfort for the drivers. To implement this smart method, electric vehicle owners must fill in an information sheet that contains the number and lengths of the their next. Then, its required energy is calculated this info, and the initial battery state of charge. Finally, the charging management system starts the planning according to its upstream distribution system operator. Several parameters are needed for the planning such as time of use tariff of the electricity, the distribution transformer or substation physical limits, the rate of the charge (normal or fast), etc. The aim of the planning is to minimize the charging cost considering the technical and economic constraints. Here, the YALMIP and MOSEK software are used as the solver of the proposed mixed-integer linear programming model.

Energy management of multi-microgrids based on game theory approach in the presence of demand response programs, energy storage systems and renewable energy resources

In the deregulated power market, decentralized scheduling of microgrids is a complex problem with many technical and economic barriers. Achieving a comprehensive model that satisfies all technical, security and economic constraints and prevents market power is one of the concerns of distribution system operators. Therefore, this paper presents a two-stage framework in which DFR is implemented to satisfy technical and security constraints and microgrids day-ahead scheduling is done by a game-theoretic approach to avoid market power. The proposed model is implemented on a modified 69-bus distribution network by implementing a shiftable-load demand response (DR) program. The problem is formulated as a two-stage model: In the first stage, the distribution feeder reconfiguration (DFR) problem is solved to reduce losses, while in the second stage, the Disco and microgrids day-ahead scheduling problem is solved by a multi-objective optimization method based on the game theory with regard to the obtained topology. The optimization problem is also modeled as a mixed-integer quadratic programming (MIQCP) problem, solved using the CPLEX solver in GAMS software. The results show the high impact of the optimal topology on the decrease in loss, voltage deviation, and operation costs. In addition, the operation results indicate that the multi-objective optimization based on the game-theoretic method not only reduced market power, but also reduced operating costs by 23.7%.

Optimal sizing of distributed generation units and shunt capacitors in the distribution system considering uncertainty resources by the modified evolutionary algorithm

Distributed generation units (DGUs) as auxiliary sources of power generation can play an effective role in meeting the load consumption of the distribution network, also have positive effects such as reducing loss and improving voltage. Moreover, capacitors by reactive power compensation produce positive effects similar to DGUs in the distribution networks. The idea of joint operation of DGUs and shunt capacitors (SCs) in the presence of demand response program (DRP) to derive maximum benefits from their installation is proposed in this paper. The time of use (TOU) mechanism is used as one of the demand response programs (DRPs) to alter the consumption pattern of subscribers and improve the performance of the distribution system. Objective functions include minimization of energy loss, operational cost, and energy not supplied (ENS). In general, the problem of determining the optimal capacity of DGUs and SCs is complex due to the demand variation. Also, considering the effect of uncertainty sources complicate the optimization problem. Hence, a modified shuffled frog leaping algorithm (MSFLA) is proposed to overcome the complexities of this problem. The proposed approach is tested on two 95, and 136-node test networks, and the results are compared with other evolutionary algorithms. According to the obtained results, after using the proposed approach in determining the optimal capacity of DGUs and SCs in the first system, the amount of energy loss, operational cost and ENS dropped by 11, 25.5 and 5% compared to baseline values. After applying the TOU mechanism in allocation of DGUs and SCs simultaneously in the second system by proposed method, the values obtained for the mentioned objectives reduced by 29, 65 and 7% compared to initial values.

Energy management considering simultaneous presence of demand responses and electric vehicles in smart industrial grids

Due to a growing trend in electricity consumption and limitations in expanding the capacity of power plants, the issue of privatizing and restructuring the electricity industry has come to the fore. In this regard, with the emergence of competitive markets, drawing upon responsive programs and electric vehicles in planning smart networks has become an intense topic of research. This paper presents a new method for programming industrial virtual power plants (IVPP) considering synchronous presence of demand response programs and electric vehicles. The proposed algorithm aims at maximizing the proceeds from industrial networks through demand response planning and managing the energy consumption of electrical vehicles. The algorithm is implemented in Zone 2 of the modified version of the IEEE Reliability Test System. The simulation results show that the presence of electric vehicles in the parked position will increase the storage capacity of the IVPPs, whereupon the consumption load on IVPPs is reduced. Using demand response programs in this regard and selecting the best program for each IVPP at different times of the day cuts back on both network operating costs and high consumption peaks, whereby an improvement in the efficiency of electric vehicles parking schemes is obtained.

A new fuzzy model for investigating the effects of lightning on the risk-based self-scheduling strategy in a smart grid

The possibilistic-probabilistic self-scheduling problem is addressed in this paper to maximize the profit of a producer from several conventional units’ production, a wind turbine production and an Energy Storage System (ESS). Demand Response Program (DRP) is employed to reduce the uncertainty cost of the wind turbine production due to the wind speed uncertainty. Moreover, the unit commitment security constraints as well as the emission constraints are modelled as the restrictions of the proposed problem. This paper proposes a fuzzy-Markov model for modelling the effects of lightning uncertainty on the performance and self-scheduling of a producer in a smart grid in the presence of DRPs, renewable energies and ESSs. The DRPs and ESSs could be considered as the fast response resources and they play an important role in providing electricity demand and increasing the flexibility of the smart grid in the time of the occurrence of high-impact, low-probability events such as lightning. The generation units’ aging and its effect on self-scheduling problem are modelled. The problem is evaluated through different scenarios considering different amounts of risk levels. The risks associated with the uncertainty of the renewable resources and market prices are modelled using Conditional Value at Risk (CVaR). The mixed integer non-linear programming has been used to solve the self-scheduling problem and the problem is implemented in GAMS software and solved by COUENNE solver. The results show the effectiveness of the presented fuzzy technique for modeling the lightning uncertainty and the proposed method for solving the self-scheduling problem.

Resiliency-oriented optimal scheduling of microgrids in the presence of demand response programs using a hybrid stochastic-robust optimization approach

Natural incidents with a low occurrence probability and high impacts like windstorms and earthquakes, threaten the optimal operation of the distribution networks. Hence, some operational solutions are required to improve network resiliency. Microgrids (MGs) provide a proper solution for the resiliency enhancement of distribution networks to reach the optimal resilient operation under such incidents. Also, the inherent uncertainties make the resilience-oriented optimal operation of MGs more critical and should be modelled by efficient uncertainty modelling tools. In this paper, a hybrid stochastic-robust optimization (HSRO) approach is used to get the optimal scheduling of an MG under normal and resilient operation mode. The impact of the upstream grid price uncertainty on the optimal scheduling of MGs is modelled using the robust optimization approach. Other dominant uncertainties, including the wind power, photovoltaic (PV) power, and active/reactive electrical loads, are also modelled by stochastic optimization via generating a set of relevant scenarios. In addition, adjustable and interruptible demand response programs (DRP) are implemented to improve the resilient operation of MG under incidents. The proposed approach tries to improve the MG operation under uncertainties during the resilience and normal mode of network. Furthermore, this paper concentrates on the robust and normal strategies adopted for the resilience-oriented scheduling of MGs. The effectiveness of the proposed framework is evaluated through a large-scale MG test system.

Optimal integration of Demand Response Programs and electric vehicles into the SCUC

Abstract Security Constrained Unit Commitment (SCUC) is carried out by power system operators to determine the optimal participation of generating units to meet the load demand while satisfying units’ and systems’ constraints as well as the desired level of security. With technological advances in the realm of communications and development in the infrastructure, a proper environment for the active contribution of customers has been formed. This new structure has enabled the consumers to play a vital role in the market via Demand Response Programs (DRPs) and coordinated aggregated Plug-In Electric Vehicle (PEV) fleets. These new resources could be a merit for the power system provided that they are employed correctly and in coordination with the rest of the system. The SCUC is where DRPs and PEVs could be optimally addressed to exploit their full potential. This paper tackles the problem of SCUC in the presence of DRPs and aggregated PEV fleets. The objective is to lessen the operation cost provided that all constraints are satisfied. Different constraints including those related to the system, units, transmission system, DRPs, and PEVs as well as the security constraints are taken into consideration. The SCUC is modeled as a two-stage stochastic mixed-integer programming problem and an appropriate model of PEVs is derived to facilitate the integration of these resources into the grid while considering their charging and discharging characteristics as well as efficiency. Moreover, a suitable model of DRPs is introduced in this study based on the concept of price elasticity of demand. The proposed approach is practiced over the IEEE 24-bus Reliability Test System (RTS) to analyze the effect of the smart grid on SCUC. Obtained results demonstrate that with proper deployment of DRPs and PEVs the operation cost could be lessened while the security of the system is improved.

Optimal eco-emission scheduling of distribution network operator and distributed generator owner under employing demand response program

This paper proposes an optimal operational scheduling framework to integrate the distributed generators (DGs) in the distribution network. This framework is used to maximize the benefit of DG owner and distribution network operator (DNO). In this paper, two optimization model has been proposed to optimum emission and economic operation performance of distribution network in the presence of demand response program (DR). DR under time of use (TOU) pricing is utilized to promote both DG owner and DNO benefits from economic operation issue. The mixed-integer programming (MIP) is used to model a multi-objective problem in General Algebraic Modeling System (GAMS). Then, the problem is solved by employing weighted sum and fuzzy decision making methods. The obtained results reveal that due to positive implementation of DR program, dependency of the distribution network to the upstream network is decreased and load curve become smoother. The under study systems are IEEE 33-bus test system and 101-bus Khoy-Iran actual distribution system which compose electric vehicle parking lot (PL), battery storage, hydrogen storage system (HSS), and local dispatchable generators (LDG).

Optimal allocation of unified power quality conditioner in the smart distribution grids

The optimal operation of current distribution networks has a great importance due to growing electrical demand and significant power losses. In this paper, the optimal allocation of unified power quality conditioner (UPQC) in smart grid with responsive loads is investigated as an appropriate solution to reduce power losses. Also, in order to consider different consumption patterns and to predict the demand with the lowest error, different linear and nonlinear models of responsive loads are taken into account. New pricing method in demand response programs (DRPs) is another concept which is developed in the paper by which optimal electricity prices in DRP during peak, off-peak, and valley periods are determined. Moreover, DRPs are prioritized by the Topsis method based on the different Utility’s policies. The final proposed model has been applied on the IEEE 12-bus, IEEE 33-bus, and the practical 94-bus Portuguese RDS distribution system. The results show that the UPQC allocation in the presence of DRPs is a win–win game both for the Utility and for customers.

Risk-averse stochastic operation of a power system integrated with hydrogen storage system and wind generation in the presence of demand response program

Wind generation (WG) units as renewable energy sources (RESs) are increasing in the world due to environmental functions and lack of conventional energy sources. Also, hydrogen storage system (HSS) as an energy storage system (ESS) is used to cope with variable nature of RESs in which the concepts of power to hydrogen (P2H) and hydrogen to power (H2P) are defined. In this work, a risk-averse stochastic operation of HSS and WG is modeled using a scenario-based stochastic approach by considering price-responsive demand response (DR) program. All uncertainties are modeled via a scenario-based stochastic approach while the risk related uncertainties are modeled via the downside risk constraints (DRC) to capture the risk-averse operation of the HSS and WG. In order to investigate the impact of DRC implementation, a risk-averse strategy is compared versus risk-neutral strategy. Compared results show that the risk-in-cost (RIC) is reduced while the expected operation cost (EOC) is raised to deal with the risk of the uncertainty.