Incentive-based Demand Response Research Articles

Comprehensive evaluation of novel coupon incentive-based demand response for different entities in energy markets

Comprehensive evaluation of novel coupon incentive-based demand response for different entities in energy markets

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network.

Reinforcement learning model for incentive-based integrated demand response considering demand-side coupling

Incentive-based integrated demand response has great potential in addressing supply-demand imbalance in integrated energy system. This study first proposes an incentive-based integrated demand response model based on multi-agent reinforcement learning, to deal with the multi energy coupling on the demand-side and enhance the applicability of the incentive-based demand response program in urban integrated energy system. Then, a self-adaptive method is proposed based on consumer energy consumption behavior, which utilizes nonlinear incentive strategy to encourage consumers to provide more demand response resources. Finally, the experimental results show that considering demand-side coupling can enhance the overall benefits of incentive-based integrated demand response, achieving a win-win situation between integrated energy service provider and consumers. After implementing incentive strategy that considers demand-side coupling, profit of integrated energy service provider increased by 16.27% and the discomfort costs borne by integrated energy consumers reduced by 11.5%. This study offers a promising approach to managing the complex and variable energy consumption behaviors in urban integrated energy system, contributing to more reliable and efficient operation of urban integrated energy system.

An integrated price- and incentive-based demand response program for smart residential buildings: A robust multi-objective model

Residential buildings consume a significant amount of energy, emphasizing the importance of optimizing energy usage. Demand-side management (DSM) helps consumers and producers manage energy consumption through incentives and pricing. This study develops a new mathematical model to manage DSM in smart residential buildings. Extant literature commonly considers only a single objective function, ignores uncertainties, and applies only one price- or incentive-based program to load management in smart residential buildings. This study develops a multi-objective mixed-integer linear programming (MILP) model that applies both price- and incentive-based programs and considers uncertainties. The objectives are cost reduction, peak load minimization, user comfort improvement, and load factor maximization. This model can manage optimal schedules for household appliances and power exchange within buildings. The study shows that participating in the incentive-based program in a four-household residential complex yielded a 2 % decrease in electricity costs and a 1 % reduction in peak load while upholding comfort and load factor levels compared to non-participation. When extended to an eight-household complex, potential benefits include an 8.3 % decrease in electricity cost and a 2.6 % reduction in peak load, highlighting the program’s effectiveness in residential energy management strategies.

Impact of maximized utility benefit based on customer willingness for economic operation of a grid connected microgrid system

Optimal scheduling of distributed energy resources (DERs) to obtain a minimized generation cost of a low voltage (LV) microgrid (MG) system has always gravitated the power system optimization researchers. This paper proposes a dual level optimization framework which emphasizes on maximizing the DISCOM profit in the first level and later minimizes the generation cost of the MG system. The MG system considered for the study consists of renewable energy sources, fossil fueled generators, micro turbine and fuel cell and the cost components involve depreciation cost, fuel cost, penalized emission cost and operation and maintenance (O&M) cost. An incentive based demand response (IBDR) policy is implemented which utilizes customer’s willingness to curtail their load demands and offers incentives for the same. When the DISCOMs share of profit is maximized using IBDR policy in level one, the modified load demand of the MG system obtained by deducting the curtailed load from the forecasted load is considered to minimize the generation cost of the MG system in the second level. A recently developed swift and facile Arithmetic optimization algorithm (AOA) is used as optimization tool for the study. The maximum DISCOM benefit was found to be 211▪ when the customers curtailed 105 kWh energy as per their willingness. Literatures reported the minimum power generation cost of the MG system as 880▪, 871▪ and 807▪ for base load, load shifting based demand side management (DSM) and customer incentive-based DR policies respectively. The proposed IBDR policy outperformed the reported literatures with a minimal generation cost of 768▪. Measures of central tendencies also corroborate to the robustness and efficiency of AOA.

Differentiated pricing for the retail electricity provider optimizing demand response to renewable energy fluctuations

As renewable energy expands, its fluctuations challenge grid security. Demand response (DR) is a crucial solution for ensuring grid stability. This paper designs a two-stage optimization model for the retail electricity provider (REP), aiming to develop differentiated prices to incentivize multiple types of users in DR. In the first stage, the REP utilizes energy storage to adjust electricity purchases. In the second stage, a bi-layer optimization model is constructed. The REP participates in incentive-based DR and sets differentiated prices for lower-level users. Based on these prices, multiple types of users modify their electricity consumption behavior, aiming to maximize the combined satisfaction with the comfort and economy of electricity consumption. Finally, the Karush-Kuhn-Tucker (KKT) method is used to derive the equilibrium solution between user response quantity and price. The results indicate that (1) Combining energy storage and user-side DR boosts responsiveness by 28.5% compared to storage alone and 23.2% compared to user-side DR alone. (2) Energy storage allows the REP to create more compelling prices for residential users. (3) Differentiated pricing boosts industrial users participating in DR, resulting in over 50% higher response rate than uniform pricing. This study provides policy implications for grid managers to develop effective DR programs.

Effective incentive based demand response with voltage support capability via reinforcement learning based multi-agent framework

With the ongoing advancements in power grids, it is crucial to maintain a balance between energy generation and usage to ensure the stability of power systems. Any disparity between the supply and demand of energy can lead to increased expenses for utility companies and consumers, which might potentially result in a widespread failure of the grid. To tackle this problem, this study offers the implementation of the Voltage Capability Incentive-Based Demand Response (VC-IBDR) system, which utilizes a multi-agent framework. The main goal is to reduce power usage by providing financial incentives, while also ensuring that voltage standards are satisfied at each node. In this method, the aggregator agent coordinates demand response operations by interacting with household agents that are equipped with home energy management systems. Every household utilizes a disjunctively constrained knapsack problem optimization method to effectively organize the consumption of home appliances, considering customer preferences and dissatisfaction. During demand response events, the aggregator agent works together with household agents to optimize appliance schedules, ensuring that voltage levels at each node remain within acceptable levels. The simulation is performed to assess the efficacy of VC-IBDR on a feeder that provides service to 25 residential households. The findings indicate substantial decreases in power usage, with an average decline in demand of 4.20% at the feeder level and a 10.41% decrease in peak power consumption throughout the day. Furthermore, the voltage values at each node stay consistently steady within the range of 0.96 to 1.0 per unit throughout the day.

Deep reinforcement learning based energy management of a hybrid electricity-heat-hydrogen energy system with demand response

Hybrid electricity-heat-hydrogen energy system with demand response (DR) is promising in enhancing flexibility and energy efficiency. However, the multi-energy coupling and source-load uncertainties makes it challenging to efficiently schedule energy flows of electricity generation, storage and DR. To this end, this paper proposes a continues deep reinforcement learning algorithm, specifically the deep deterministic policy gradient (DDPG), for the energy management optimization. Different Markov decision processes are firstly employed to analyze and compare two kinds of incentive-based electro-thermal DR contracts, i.e., load curtailment and load shifting. Simulation results exemplify the superiority of the proposed DDPG-based scheduling incorporating electro-thermal DR in terms of economy and sustainability, leading to a 16.02 % reduction in scheduling costs for contract load curtailment and an 8.52 % reduction for contract load shifting when compared to that without DR consideration. Furthermore, the robustness of DDPG-based scheduling is verified under 60 random source-load scenarios compared with different algorithms. Compared to results obtained by DDPG, DDPG-LC and DDPG-LS reduce the mean cost by 22.15 % and 12.84 %, while their error from the theoretical optimum is only around 5 %. The results demonstrate that the approximate optimality and rapid decision-making illustrate DDPG's efficient real-time scheduling capability, thereby enhancing the system's adaptability to uncertain environments.

Improvement of Economic Integration of Renewable Energy Resources through Incentive-Based Demand Response Programs

The integration of renewable generation presents a promising venue for displacing fossil fuels, yet integration remains a challenge. This paper investigates Demand Response (DR) as a means of economically integrating Renewable Energy Resources (RERs). We propose Incentive-Based DR (IBDR) programs, particularly suitable for small customers. The uncertainties in the electricity market price pose a challenge to IBDR programs, which is addressed in this paper through a novel and robust IBDR approach that considers both the electricity market price uncertainties and customer responses to incentives. In this paper, scenarios are simulated premised on the Western Electricity Coordinating Council (WECC) 240-bus system in which coal-fired power plants become inactivated, while the RER contribution increases in the span of one year. The simulation results indicate that the proposed IBDR program mitigates the issues associated with renewable expansion, such as utility benefit loss and market price volatility. In addition, the proposed IBDR effectively manages up to 30% of errors in day-ahead wind forecasts that significantly reduce financial risks linked to IBDR programs.

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.

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.

Design and implementation of smart home energy management system for Indian residential sector

Residential power consumption has surged with the widespread adoption of smart appliances, prompts utilities to implement Demand Response(DR) programs. Home Energy Management Systems (HEMS) plays a vital role in these initiatives within the smart grid infrastructure. The rapid advancement of wireless communication technologies, especially the Internet of Things (IoT), will enable successful architectural designs for HEMS. This study proposes an incentive-based DR implementation using IoT-enabled HEMS for the Indian residential sector that represents 25% of the total power demand. The proposed HEMS automates the management of categorized loads during peak hours through load shifting and scheduling. The proposed algorithm is deployed in real-time residential setup using IoT modules. Emphasizing sustainability and consumer affordability, the work integrates a Photo-Voltaic (PV) based Battery Storage System (BSS) to support critical loads during peak hours. Consumer load preferences obtained through a survey are incorporated into the deployment of the proposed HEMS. Experimental studies demonstrated significant energy savings of 49% with the loads under consideration and substantial cost savings like approximately 49% with Load Management (LM) alone, 53% with the inclusion of Time of Day (ToD) tariff, and an impressive 78% with the proposed incentives for participating in DR. Additionally, this study highlights the synergistic savings realized through the combined implementation of the proposed LM strategy and ToD tariff in India.

A PEM-based augmented IBDR framework and its evaluation in contemporary distribution systems

Demand response (DR) is an attractive concept that invites customers’ active participation in the distribution sector by means of price elasticity of demand (PED). It not only enhances customers’ demand sensitivity but also improves technicalities and economics related to both the utility and demand sides. This paper emphasizes the combined effect of price-based DR (PBDR) and incentive-based DR (IBDR) with the inclusion of PED. The elasticity phenomenon, when applied with incentives as in IBDR, changes the demand-consumption pattern as compared to individual DR. Moreover, the demand variation due to only incentives leads to incentive elasticity, which needs to be studied carefully; then only the impact of individual DR and augmented DR (PBDR and IBDR combined) can be understood analytically. In this work, IBDR models are tested on considered pricing schemes along with a new proposed pricing scheme to evaluate the systems’ technical and economical parameters. A standard IEEE 33 bus distribution system has been chosen for the assessment of suggested models and to compare them to the existing ones. Furthermore, these models are descriptively evaluated from both the utility and consumer perspectives.

Optimal allocation of customer energy storage based on power big data and improved LSTM load forecasting

This research explores the potential of energy storage investment with a focus on regional power users. An incentive-based demand response framework is constructed, emphasizing the aggregation of demand-side resources for active participation in the power market. Utilizing a data-driven approach, the improved Long Short Term Memory (LSTM) model is employed to predict customer behavior in response to incentives. The primary objective is to maximize the life cycle benefit while minimizing the payback period for users investing in energy storage. By harnessing big data analytics, suitable users for energy storage investment are identified and optimal capacity allocation is determined. Given the current energy storage parameters and dynamics of electricity pricing, boundary values for profitable user energy storage capacities are derived. This methodology filters users with capacities exceeding these thresholds, marking them as potential candidates for energy storage deployment. Furthermore, a measurement model for iron phosphate batteries is introduced. Analysis of adjustable resource capacity, duration, and benefits for potential users provides insights into optimal energy storage investment strategies. Integrating configured energy storage batteries with time-of-use tariffs substantially reduces energy storage costs, leading to enhanced economic efficiency.

Optimal Power Dispatch for a Chlorine Factory With Fuel Cells Participating in Incentive-Based Demand Response

Optimal Power Dispatch for a Chlorine Factory With Fuel Cells Participating in Incentive-Based Demand Response

Optimal scheduling of the CSP–PV–wind hybrid power generation system considering demand response

In order to solve the problem of there being a high proportion of wind and photovoltaic (PV) abandonment in the new energy system, an optimal dispatching method of concentrated solar power (CSP)–PV–wind hybrid power generation considering demand response is proposed. First, on the basis of the combination of wind and PV power stations and CSP stations, electric heating (EH) devices are used to convert unconsumed wind and PV power into thermal energy stored in the thermal storage system of the CSP station. Second, based on the difference in response time and response volume of customers, a stepped incentive-based demand response (IBDR) approach is introduced. Thus, a CSP–PV–wind power optimal dispatch model taking into account IBDR is established to achieve coordinated source-load dispatch. Finally, the CPLEX solver is invoked to optimize day-ahead scheduling with the goal of the lowest system comprehensive cost. Example analysis is used to confirm the proposed method’s usefulness in enhancing the system’s wind and PV power consumption capacity and reducing the system’s comprehensive cost.

Droop parameter-based economic dispatch for islanded microgrid considering demand response

The droop parameter setting of the distributed generator (DG) has an essential role in islanded microgrid (MG) systems. This paper presents droop-based economic load dispatch (ELD) in an islanded MG. In this approach, the Power–frequency (P–f) droop characteristic of DG is integrated into its incremental cost, which is the derivative of the cost function of DG. The optimisation problem is formulated by considering the standard deviations of the incremental costs of DGs. Under steady-state conditions, DGs operate at a single frequency and the P–f droop-based ELD ensures that the incremental costs of DGs are equal, minimising the overall cost of active power generation, following the equal incremental cost principle. Furthermore, an incentive-based demand response (DR) programme is integrated into the system to maximise the utility benefit function by incentivising customers for power curtailment. The results exhibit significant power curtailment during peak load hours, indicating the effectiveness of the DR programme. The proposed combined mathematical model is applied to the modified 33-bus islanded MG and solved using basic particle swarm optimisation. It calculates optimal droop parameters for a day-ahead scenario and emphasises the efficiency benefits of optimised droop settings and the integration of DR in islanded MGs.

Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection

Considering the flexible capacity and privacy needs of numerous flexible energy users in the context of demand response (DR), this study establishes an influence model (IM) to describe the DR participation capabilities of users considering privacy budget. Using the designed Stackelberg game mechanism that can achieve optimal selection for DR responders from users with different characteristics described by IM, a federated learning (FL)-based optimization method that uses differential privacy (DP) as the data transmission mechanism for DR economic optimal dispatch is proposed. Ideas of controlling the number of participating users and financially compensating for the privacy leakage risk by the FL-based optimization method are the guarantees for users with private data to participate in DR. The performance of the proposed optimization method is also compared with that of the Moth-flame optimization algorithm in a case study, and the guiding value of the former in selecting among user groups with different characteristics is then discussed. Results show that the proposed method exhibits good economic benefits and universal applicability.

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.

A Multilevel Model of Energy Market Considering Coupon Incentive Based Demand Response with Wind Power Uncertainty

Demand response (DR) can decrease or increase energy consumption and has the ability to use generating assets more efficiently. Because of the increased power consumption, it has become necessary for the power system networks either to update or upgrade the already existing power system or to inculcate some schemes to minimize the load through effective customer engagement. To address this issue incentive-based demand response proved to be an effective tool to change the pattern of consumption. This work proposes a novel multilevel energy market considering coupon incentive based DR (C-IBDR). The proposed model is operated on a modified PJM 5-Bus system where the independent service operator (ISO) using economic dispatch (ED) computes the localized marginal pricing (LMP) and the load serving entity (LSE) issues coupons to consumers and procures the demand reduction from them. Two wind generators are incorporated into the test system and their effect on the localized marginal pricing with and without demand response is analyzed. The optimization problems are modeled using particle swarm optimization (PSO) and linear programming (LP) in MATLAB. The results demonstrate that the proposed C-IBDR reduces peak demand from 900 to 860 MW through the utilization of wind and DR. This alteration modifies the load curve, increases the power factor from 0.524 to 0.555, raises the revenue of LSEs from $106,646 to $106,946, reduces generation costs from $473,291 to $214,651, and procures a total of 262 MW of demand from the consumer side in the form of demand reduction. The system is further tested for scalability. The practicality of the suggested approach in handling more extensive systems is vividly demonstrated through its application to the modified IEEE 118-bus system.