Incentive-based Demand Response Programs Research Articles

A comprehensive reserve allocation method in a micro-grid considering renewable generation intermittency and demand side participation

Due to the growth of renewable energy sources in power distribution networks, intermittent nature of these resources can reduce the reliability of the system caused by the lack of precise determination of required reserve in some hours of operation. In this paper, a new formulation of reliability indices is proposed involving the forecast errors of the renewable generations and multiple outage events of thermal and renewable units. The developed indices are applied to more accurate stochastic reserve allocation in a micro-grid. The customers are considered to participate in both energy and reserve day-ahead scheduling through price-based and incentive-based demand response programs. A new bi-level method incorporating particle swarm optimization and differential evolution algorithms is proposed to solve the problem. At the first level, optimal energy and reserve offers are found from the bids of demand side participants to maximize the profit of the customers and used in the second level to minimize the operating cost of the micro-grid. Obtained results from a case study on a typical micro-grid test system demonstrated that the proposed method allocate the optimal reserve to each provider in each reliability level defined by system operator, despite the intermittent and uncertain output of the renewable resources.

An improved incentive-based demand response program in day-ahead and intra-day electricity markets

By advancement and vogue of smart grid technologies, there is a strong attitude toward utilizing different strategies for participating in demand response (DR) programs in electricity markets. DR programs can be classified into two main categories namely incentive-based programs (IBPs) and time-based rate programs (TBRPs). In this paper, an improved incentive-based DR (IBDR) model is proposed. In our proposed IBP, the concept of elasticity is improved where it depends not only on the electricity price, but also is a function of consumption hour and customer type. In this program, the incentive value which is paid to the participating consumers is not a fix value and relates to the peak intensity of each hour. The proposed IBP can participate in both of day-ahead and intra-day electricity markets. The property of considering intra-day market enables consumers to provide maximum DR if possible. The proposed model is implemented on peak load curve of Spanish electricity market and a 200-unit residential complex. Different scenarios are considered to show effectiveness of the proposed DR model from various aspects including peak shaving as well as economic indices.

Smart distribution system operational scheduling considering electric vehicle parking lot and demand response programs

Electric vehicle (EV) technology with a vehicle to grid (V2G) property is used in power systems to mitigate greenhouse gas emissions, reduce peak load of the distribution system, provide ancillary service, etc. In addition, demand response (DR) programs as an effective strategy can provide an opportunity for consumers to play a significant role in the planning and operation of a smart distribution company (SDISCO) by reducing or shifting their demand, especially during the on-peak period. In this paper, the optimal operation of a SDISCO is evaluated, including renewable energy resources (RERs) along with EV parking lots (PLs). RER and PL uncertainties and a suitable charging/discharging schedule of EVs are also considered. Furthermore, price-based DR programs and incentive-based DR programs are used for operational scheduling. To achieve this aim, a techno-economic formulation is developed in which the SDISCO acts as the owner of RERs and PLs. Moreover, DR programs are prioritized by using the technique for order preference by similarity to ideal solution method. In addition, a sensitivity analysis is carried out to investigate different factors that affect the operational scheduling of the SDISCO. The proposed model is tested on the IEEE 15-bus distribution system over a 24-h period, and the results prove the effectiveness of the model.

Integration of smart grid technologies in stochastic multi-objective unit commitment: An economic emission analysis

This paper proposes a stochastic multi-objective unit commitment (SMOUC) problem incorporating smart grid technologies (SGTs), namely, plug-in electric vehicles (PEVs), demand response programs (DRPs), compressed air energy storage (CAES) units, and renewable distributed generations (DGs). An economic emission analysis of the proposed SMOUC problem with the SGTs is carried out to minimize the total expected operation cost and emission using a new mixed-integer linear programming (MILP) method. A two-stage stochastic programming method is used for dealing with the uncertain nature of power generation from the renewable DGs. Lexicographic optimization in combination with hybrid augmented-weighted ε-constraint method are employed to obtain Pareto optimal solutions of the SMOUC problem, and a fuzzy decision making is applied to select the most preferred non-dominated solution. Besides, mathematical modeling of responsive loads can help the independent system operator (ISO) to use a conservative and reliable model to have lower error in load curve characteristic estimation, such as variation in peak load. In this regard, this paper also contributes to the existing body of knowledge by developing linear and nonlinear economic models of price responsive loads for time-based DRPs (TBDRPs), as well as voluntary and mandatory incentive-based DRPs (IBDRPs) based on the customer’s behavior (CB) concept and price ratio (PR) parameter. Also, new mathematical indices are proposed to choose the most conservative and reliable economic model of price responsive loads. Moreover, different widely used DRPs are analyzed and prioritized using the strategy success index (SSI) from the ISO viewpoint to determine the most effective DRP which has more coordination with the SGTs. The proposed MILP-based SMOUC problem with integrated SGTs, is applied to IEEE 10-unit test system and is implemented in General Algebraic Modeling System (GAMS) environment. Simulation analyses demonstrate the effectiveness of integrating SGTs into the proposed SMOUC problem from the economic, environmental, and technical points of view.

Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs

The operation problem of a micro-grid (MG) in grid-connected mode is an optimization one in which the main objective of the MG operator (MGO) is to minimize the operation cost with optimal scheduling of resources and optimal trading energy with the main grid. The MGO can use incentive-based demand response programs (DRPs) to pay an incentive to the consumers to change their demands in the peak hours. Moreover, the MGO forecasts the output power of renewable energy resources (RERs) and models their uncertainties in its problem. In this paper, the operation problem of an MGO is modeled as a risk-based two-stage stochastic optimization problem. To model the uncertainties of RERs, two-stage stochastic programming is considered and conditional value at risk (CVaR) index is used to manage the MGO’s risk-level. Moreover, the non-linear economic models of incentive-based DRPs are used by the MGO to change the peak load. The numerical studies are done to investigate the effect of incentive-based DRPs on the operation problem of the MGO. Moreover, to show the effect of the risk-averse parameter on MGO decisions, a sensitivity analysis is carried out.

Optimal energy management of a renewable-based isolated microgrid with pumped-storage unit and demand response

Abstract This paper proposes a new stochastic optimization framework for day-ahead (DA) energy management of a renewable-based isolated rural microgrid. Non-dispatchable distributed generations (DGs) including wind and photovoltaic (PV) power plants have been operated by the microgrid in order to meet consumers' demand. The main challenge in this condition is to keep balance between generation and demand at all times. In order to cope with this challenge, a pumped-storage unit and an incentive-based demand response program have been used in this work. Moreover, the accurate model of pumped-storage unit, responsive demand and distribution network and also the inherent uncertainty of wind and PV power generation have been considered in this paper. Finally, the effectiveness of the proposed optimization framework has been evaluated through extensive numerical studies. The results show that the pumped-storage unit optimal scheduling and demand response implementation, significantly improve economic and technical performance indexes.

Design of capacity incentive and energy compensation for demand response programs

Variability and Uncertainties caused by renewable energy sources have called for large amount of balancing services. Demand side resources (DSRs) can be a good alternative of traditional generating units to provide balancing service. In the areas where the electricity market has not been fully established, e.g., China, DSRs can help balance the power system with incentive-based demand response programs. However, there is a lack of information about the interruption cost of consumers in these areas, making it hard to determine the rational amount of capacity incentive and energy compensation for the participants of demand response programs. This paper proposes an algorithm to calculate the amount of capacity incentive and energy compensation for demand response programs when there lacks the information about interruption cost. Available statistical information of interruption cost in referenced areas is selected as the referenced data. Interruption cost of the targeted area is converted from the referenced area by product per electricity consumption. On this basis, capacity incentive and energy compensation are obtained to minimize the payment to consumers. Moreover, the loss of consumers is guaranteed to be covered by the revenue they earned from load serving entities.

A novel incentive-based demand response model for Cournot competition in electricity markets

This paper presents an analysis of competition between generators when incentive-based demand response is employed in an electricity market. Thermal and hydropower generation are considered in the model. A smooth inverse demand function is designed using a sigmoid and two linear functions for modeling the consumer preferences under incentive-based demand response program. Generators compete to sell energy bilaterally to consumers and system operator provides transmission and arbitrage services. The profit of each agent is posed as an optimization problem, then the competition result is found by solving simultaneously Karush– Kuhn–Tucker conditions for all generators. A Nash–Cournot equilibrium is found when the system operates normally and at peak demand times when DR is required. Under this model, results show that DR diminishes the energy consumption at peak periods, shifts the power requirement to off-peak times and improves the net consumer surplus due to incentives received for participating in DR program. However, the generators decrease their profit due to the reduction of traded energy and market prices.

Evaluation of residential customer elasticity for incentive based demand response programs

A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings.

A Framework of Residential Demand Aggregation With Financial Incentives

The development of intelligent demand-side management with automatic control enables a large amount of residential demands to provide efficient demand-side ancillary services for load serving entities. In this paper, we introduce the concept of a comfort indicator, present an advanced reward system, and finally propose a framework for aggregating residential demands enrolled in incentive-based demand response (DR) programs. The proposed framework not only allocates load serving entities' demand reduction requests among residential appliances quickly and efficiently without affecting residents' comfort levels but also rewards residential consumers based on their actual participation. Also, since the framework is designed with the practical considerations of simplicity and efficiency, it can be utilized as a quick implementation for existing pilot development works. The effectiveness and merit of this framework are demonstrated and discussed in the comparison studies with conventional incentive-based DR.

A Design Method for Incentive-based Demand Response Programs Based on a Framework of Social Welfare Maximization

Demand response program (DR) is expected as one of the most effective alternatives to the traditional power supply-demand operations because it does not require additional investment in power plants and equipment. However, there is still room for discussion on setting of electricity price and rebate level with ensuring resources. This paper presents a pricing method for the DRs based on an idea of social welfare maximization. In the proposed method, decrement of the consumers’ comfort, which is caused by the DR cooperation, is calculated as the appropriate incentive payment. That is, the authors evaluate the negative consumer surplus, and convert it into the incentive payment in the DRs. Meanwhile, the limit of DR requirement is estimated in accordance with the resulting decrement of suppliers’ benefit. Through discussions on numerical results, usefulness of the authors’ proposal is verified.

Conceptual framework for introducing incentive-based demand response programs for retail electricity markets

Enduring expansions and advancement in the knowledge driven energy-economy era promise a power system that is cost-effectively competent, environmentally responsive, flawless and operationally acquiescent. This imminent power system will depend on latest communication technologies, computational easiness, and monitoring and control strategies attributable to the consumers or end users. Amid the numerous advances related to this development, a strategic and significant component is demand side management (DSM). Demand response can play an important and pertinent role in the milieu of DSM, and incentive-based demand response program (IBDRP) is getting customer focus in many parts of the world. This study tries to bring out major aspects to be considered while introducing IBDRP in retail electricity markets. Document analysis, a well-known qualitative research methodology, is used for analyzing various IBDRP schemes practiced globally, with the help of the ATLAS.ti software and utilizing the snowball sampling technique. For better understanding, the results of the axial coding are arranged in three stages: pre-implementation stage, implementation stage, and post-implementation stage. Based on the importance and their frequency of appearance, ten most prominent codes were identified, and the strength of their interrelationships is presented on a multilevel scale with three different ranks. One of the major and important outcomes of this research is that, with the help of the proposed conceptual framework, a proper implementation framework can be built, which can be used for developing most appropriate IBDRP for any retail electricity market.

The impact of customers’ participation level and various incentive values on implementing emergency demand response program in microgrid operation

Customers play a key role in running Demand Response Programs (DRPs). Incentive-based DRP is defined as changes in costumers’ consumption level in response to incentive programs outlined by systems to reduce the daily load demand peaks. Optimal operation of microgrids is one of the most important challenges of power system operators. Decreasing the cost of generation and operation while the constraints are met is one of the main objectives of this problem. In this article, the impact of customers’ participation level in Emergency Demand Response Program (EDRP) and the effect of illogical incentive values in implementing EDRP on the microgrid operation are represented. Moreover, the effect of applying an incentive-based EDRP, on loads and power system operation cost in presence of various uncertainties has been studied in detail. To model the uncertainties in microgrid operation such as generation unit failure, random outages of transmission lines and upstream line, load forecasting error, power generation uncertainty of renewable energies based distributed generation units, and the possibility of no response of customers to scheduled interruptions, several scenarios are considered. EDRP is modeled based on price elasticity and customer benefit function. To verify the efficiency of the proposed model, it is applied to 11-Bus microgrid in a 24-h scheduling period. Simulation results illustrate the effects of different level of customer’s participation on costumer’s benefit and total operation costs as well as determining an efficient use of energy resources in microgrid operation. It is also observed that unreasonable increase or decrease of incentives leads to increase of total cost. This gives an overview to microgrid operator to select the optimum incentive value based on the situation.

Designing interruptible load management scheme based on customer performance using mechanism design theory

Interruptible load management (ILM), as an important incentive-based demand response (DR) program, plays a critical role in power system operation. An improved ILM scheme design method is proposed in the paper. From the perspective of social welfare, the scheme design is described as an optimization problem, and then mechanism design theory is adopted for solving. The novel idea presented in the paper is that the customer baseline load (CBL) is used to parameterize the type of customer (TOC), which is fundamental information for application of mechanism design theory. Being private information, TOC is not directly attainable, but CBL can be estimated based on historical load data (namely the customer performance), and it can reflect the customers’ rational judgments in self-benefit decisions. Firstly, the validity of adopting CBL to determine TOC is demonstrated. Then, the load curtailing potential constraints and the objective function are rebuilt in the expression of CBL correspondingly, so that the mechanism design theory successfully works with CBL instead of TOC. Moreover, adjustment strategies are also proposed to cope with the possible unenforceable scheme scenarios, thus the practicability of the designed scheme is ensured. Finally, simulations are carried out to validate the effectiveness of the proposed method.

Optimizing sheddable and shiftable residential electricity consumption by incentivized peak and off-peak credit function approach

Many current studies on smart grid in electricity market are indicative of the key role it plays in electricity generation, distribution, retailing and end-user management. Demand response programs (DRPs) can be used to lower high energy prices in wholesale electricity markets, and ensure the security of power systems when at risk. The concern of most researchers in this field is to further unearth the potential of smart grid in the direction of demand response (DR) through enhanced demand side management (DSM) centering on the behavior of the end-user. Our model proposes a more effective way in using incentive based demand response program to help residential customers derive more benefits from smart grid. The constrained non-linear programming (CNLP) model optimizes residential consumption of electricity by shaving of load at peak and increasing of load at off-peak to help generators reduce production cost at peak times and increase revenue at of off-peaks. The model uses a credit function to regulate consumption and reward end-users for load shedding and load shifting at peaks and also at off-peaks reward end-users for increasing load. The simulated results show that, high consumption appliances are best used at dawn, midday, and at night, if consumers want to cut down cost. The corresponding effect is that, generating and distribution companies derive the right revenue from their investments by not producing beyond their capacity during peaks at high cost and maintain constant power supply in and environmentally friendly manner.

Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids

Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause. Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method. Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds. The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method. The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives.

Optimal use of incentive and price based demand response to reduce costs and price volatility

There are two general categories of demand response (DR): price-based and incentive-based DR programs. Each one has its own benefits taking advantage of different aspects of flexible demand. In this paper, both categories of DR are modeled based on the demand-price elasticity concept to design an optimum scheme for achieving the maximum benefit of DR. The objective is to not only reduce costs and improve reliability but also to increase customer acceptance of a DR program by limiting price volatility. Time of use (TOU) programs are considered for a price-based scheme designed using a monthly peak and off-peak tariff. For the incentive-based DR, a novel optimization is proposed that in addition to calculation of an adequate and a reasonable amount of load change for the incentive, the best times to realize the DR is found. This optimum threshold maximizes benefit considering the comfort level of customers as a constraint. Results from a reduced model of the WECC show the proposed DR program leads to a significant benefit for both the load serving entities (LSEs) and savings in customer's electricity payment. It also reduces both the average and standard deviation of the monthly locational marginal price (LMP). The proposed DR scheme maintains simplicity for a small customer to follow and for LSEs to implement.

Dynamic Economic Dispatch Problem Integrated With Demand Response (DEDDR) Considering Non-Linear Responsive Load Models

Intelligent implementation of demand response programs (DRPs) not only decreases electricity price in electricity markets, but also improves network reliability. In this paper, the dynamic economic dispatch (DED) problem has been optimally integrated with the incentive-based DRPs. Moreover, mathematical load modeling can be so effective in the load curve estimation with the lowest error. So, economic models of the linear and non-linear responsive loads (power, exponential, and logarithmic) have been developed for time-based and incentive-based DRPs and integrated with DED. Also, a procedure to select the most conservative responsive load model for the load estimation has been presented too. Also, determining the optimal incentive in the incentive-based DRPs is one of the independent system operator’s challenges. In the proposed combined model, the fuel cost is minimized and the optimal incentive is determined simultaneously. Valve-point loading effect, prohibited operating zones, spinning reserve requirements, and the other non-linear practical constraints make the combined problem into a complicated, non-linear, non-smooth, and non-convex optimization problem, which has been solved with a population-based meta-heuristic algorithm namely random drift particle swarm optimization algorithm. The proposed combined model is applied on a ten units test system. Results indicate the practical benefits of the proposed model.

Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents

With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based demand response programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.

Risk management and participation planning of electric vehicles in smart grids for demand response

Demand response (DR) can serve as an effective tool to better balance the electricity demand and supply in the smart grid. It is defined as "the changes in electricity usage by end-use customers from their normal consumption patterns" in response to pricing and incentive payments. This paper focuses on new opportunities for DR with electric vehicles (EVs). EVs are potential distributed energy resources that support both the grid-to-vehicle and vehicle-to-grid modes. Their participation in the time-based (e.g., time-of-use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Smart scheduling of EV charging and discharging activities also supports high penetration of renewables with volatile energy generation. This paper proposes a novel stochastic model from the Independent System Operator's perspective for risk management and participation planning of EVs in the smart grid for DR. The risk factors considered in this paper involve those caused by uncertainties in renewables (wind and solar), load patterns, parking patterns, and transmission lines' reliability. The effectiveness of the model in response to various settings such as the area type (residential, commercial, and industrial), the EV penetration level, and the risk level has been investigated.