Proposed Demand Response Program Research Articles

Integrated planning framework for microgrid incorporating the flexibility provision capabilities of demand response programs

The penetration of renewable energy sources (RESs) in power systems has led to an increase in the system’s flexibility needs. An integrated planning framework for long-term capacity sizing and short-term operation planning of an isolated microgrid (MG) using the flexibility capabilities provided by optimal demand response (DR) strategies is presented in different scenarios. DR programs incentive optimization is applied for omitting or reducing the mismatch between consumption and generation profiles to achieve economic investment and operation planning. The evaluated scenarios include the combination of photovoltaic (PV) system, wind system, battery, supercapacitor, and pumped heat energy storage (PHES). The objective function is to minimize the annual costs of MG design and operation. A new method is proposed for hourly pricing of DR programs based on the amount of charging and discharging of the energy storage system for isolated MGs. The combination of PV, wind, and PHES systems is found to be the most cost-effectiveness configuration for the MG. The proposed DR program reduces the total costs of MG by 15.89% and increases system flexibility.

Demand response approach in the presence of plug-in hybrid electric vehicle for profit maximization of utility

• Scheduling of battery ESS and EV in the presence of multiple DG resources. • Analysing the impact of the proposed DR during the different operating nature of battery ESS and EV as uniform and converse operation respectively. • Evaluating the proposed DR program at its full potential for profit maximization. The efficient operation of available resources and the penetration of renewable resources into the existing power network require prior and proper scheduling. The potency of Demand Response (DR) relies on its capability to revise the load demand in accordance with the conditions of the grid. The proper implementation of a DR program can reduce the import cost of the consumer and reduces the peak load of the utility which will assist in the smooth operation. The appropriate Battery Energy Storage System (BESS) and a suitable DR program aid in managing the loads to maintain a balance in the demand–supply chain. The influence of extensive integration of Electric Vehicles (EV) into the system is inevitable which also demands a suitable scheduling scheme along with all the available resources. Renewable energy generation, energy storage systems with the inclusion of EVs, and the utilization of grid power are all planned using the real-time electricity tariff profile. The profit of the system is analyzed in the presence of multiple DG resources incorporating plug-in hybrid EV and ESS along with Demand Response (DR) under the converse (any one is charging while other is discharging) and uniform (either one is charging and the other is discharging) operation. The proposed DR approach is scalable and generalized, claiming to be suitable for huge-scale renewable energy integrated power systems as well. The proposed method is tested on IEEE 33 and 69 bus systems and the accomplished results are promising.

Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid

The growth in the energy demand of the microgrid due to the inclusion of electric vehicles (EV) and other non-EV loads introduces several challenges for the operators in scheduling energy for the microgrid. The inclusion of demand response (DR) program in the operational planning of microgrid can decrease the burden on the operator, but it requires aggregators for the efficient coordination between the operator and several potential DR participants of the microgrid. In this work, an optimization model is proposed to include a novel incentive-based DR program in the energy management problem of the reconfigured grid-connected microgrid. Two different aggregators for EV and non-EV loads are included in the work as an interface between the operator and DR participants. The objective of the proposed DR program is to maximize the incentives offered to the DR participants while maintaining uniformity in terms of rewards and distress delivered to the DR participants. The proposed work is analyzed on a static model of a 33-bus grid-connected microgrid consisting of EV charging stations, renewable energy sources, and diesel generators at different locations. The microgrid is reconfigured at each operating interval to minimize the power lost in the network. The result confirms that optimality is achieved at the source, distribution, and load side of the microgrid. For a day-ahead operation, it has been found that the energy dependency of a microgrid on the utility grid and conventional energy source is reduced by 9.62% and 29.06%, respectively.

OPTIMAL CONTROL STRATEGY OF POWER GENERATION IN MICROGRIDS

The paper presents an innovative optimal control strategy solution for an energy system that consists of combining thermal and electrical energy (Combining Heat and Power - CHP) with the interconnection of Renewable Energy Sources - RES, allowing efficient hybrid energy generation. The consumer's electricity and thermal energy needs are controlled by means of a dedicated microgrid system (MG). The optimization strategy was validated by controlling the HVAC system of a building and aims to satisfy two main objectives: i) minimizing the energy absorbed from the traditional electrical network and ii) guaranteeing acceptable thermal comfort conditions. The proposed demand response program is a parameterized feedback control strategy where the parameters depend on the thermal state of the building, but also on the occupancy pattern of the microgrid.

Profit maximization in hybrid microgrid system incorporating demand response and plug in EV

Increasing concern for environment has forced people to look for alternatives that are eco-friendly, thereby increasing the penetration of renewable resources. The opportunity to ameliorate economic status of consumers by generating revenue and by maximizing profit encourages consumers to look for substitute for conventional generation techniques. Operation cost minimization and export maximization are the two stages of profit maximization. By recognizing the optimal combination of DG resources such as photovoltaic (PV), wind energy resources, battery and microturbine and incorporation of demand response (DR) program with optimal scheduling of plug-in hybrid electric vehicle (PHEV), electricity import from the grid is reduced, thereby minimizing overall operation cost. The proposed DR program reduces imported electricity in peak hours by shifting non-emergency loads, which are classified here as controllable loads to off-peak hours, resulting in reduced import cost. Import cost and profit comparison for the considered systems under different levels of DR in the presence of multiple DG resources and EV for different scenarios are investigated to illustrate the effectiveness of the proposed DR. Maximized profit of microgrid operator is obtained by implementing the proposed method in the test systems. The results obtained are propitious.

RETRACTED: Robust-based risk assessment of buildings in resiliency and self-scheduling operations for coordinated vehicle-to-home and renewable resources

• Investigating the impact of resources in grid-connected and resiliency modes • Analysis of uncertainty conditions in grid-connected and resiliency modes • The effect of DR programming in grid-connected and resiliency modes • The impact of RO procedure in grid-connected and resiliency modes One of the crucial tools for organizing energy consumption in buildings is a home energy management system that can minimize the energy cost by using mathematical algorithms. In this paper two practical and theoretical works are carried out. In the practical contribution, the smart home operation is modeled in the grid-connected and resiliency modes, by considering the unpredictable conditions in the upstream grid. In the terms of theoretical works, a comprehensive model that includes demand response (DR) and robust optimization (RO) techniques are considered in the grid-connected and resiliency operation modes, accurately. Uncertainty of energy price and wind energy production are items modeled separately by using the RO technique in the grid-connected and resiliency modes, respectively. The uncertainty of wind energy and electricity price is considered separately for four seasons, and the simulation results show that the system's performance has reduced daily operation costs. In the grid-connected mode, the system's operation cost in without-DR mode is about 16% higher than in the with-DR mode, but in resilience mode, the value is about 3% that represents the effectiveness of proposed DR program in grid-connected operation mode.

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

Demand Response for Industrial Micro-Grid Considering Photovoltaic Power Uncertainty and Battery Operational Cost

An intelligent demand response (DR) program is developed for multi-energy industrial micro-grid consisting of manufacturing facilities, photovoltaic (PV) panels, and battery energy storage system (BESS). The proposed DR program tackles the practical challenges of components in the micro-grid including industrial process represented by a discrete manufacturing production model, uncertainty of PV generation, and operational cost of the BESS. The proposed DR program optimizes day-ahead production scheduling for manufacturing facilities and operation regime for the BESS in response to time of use electricity price and probabilistic forecasting of PV power. To capture the uncertainty of PV power, a data-driven PV power probabilistic forecasting model is developed and a copula-based approach is deployed for the sampling of temporally correlated scenarios of PV power over the scheduling horizon from probabilistic forecasts. The multi-energy management optimization problem is formulated as a scenario-based stochastic nonconvex mixed integer nonlinear programming (MINLP). A hybrid optimization method integrating the evolutionary algorithm and the branch-and-bound algorithm for mixed integer liner programming is proposed to solve the nonconvex MINLP. Simulation studies illustrate that the proposed DR program efficiently reduces the operational cost for manufacturing production and releases the stress of the main grid by making full use of flexibility of all the components in the micro-grid.

Incentive determination of a demand response program for microgrids

The return on investment for a microgrid can be accelerated if the microgrid can maximize its profits, either by minimizing the cost of energy production or maximizing the revenue from selling electricity to the microgrid customers. This can be achieved by implementing demand response. Under a demand response program, microgrid loads can be re-scheduled from peak to off-peak periods or shaved and shed during peak periods. Moreover, demand response execution may reduce customers’ comfort; thus, the microgrid operator should offer some compensating incentives to the participants. This study has been conducted from a microgrid owner’s perspective, aiming at determining the demand response incentives for its customers which should be feasible for both demand response participants and the microgrid operator. The incentives are derived from the difference between the microgrid’s profits before implementing the demand response program and its projected benefit before implementation. Due to the effects of controlling customers' loads to the customers comfort and economic aspects, the demand response is also optimized to minimize the number of affected loads and customers’ discomfort. The given incentive varies based on the participants' discomfort level and the load’s economic value. The results show that the microgrid operating under the proposed demand response program is able to increase its profits, part of which is allocated to the consumers as an incentive to participate in the program. Furthermore, the results from the sensitivity analysis show that the pay-back period of the participants’ demand response deployment cost is within the project lifetime.

Energy and uncertainty management through domestic demand response in the residential building

An optimal energy management is addressed in the residential building. The residential building is equipped with renewable energies including wind turbines and solar panels. The uncertainty of renewable energies is modeled by stochastic programming. The demand response program is simultaneously adopted to handle such uncertainty and reducing the energy cost. In this respect, four different loads are modeled in the building including interruptible, constant energy, constant power, and uninterruptible loads. The aforementioned loads are properly adjusted and dispatched for minimizing the energy cost as well as to deal with renewable energy intermittency. The bidirectional operation is modeled for the building and it can send energy to the grid or receive it from the upstream network. The results verify that the introduced model can efficiently harvest all possible energy of the wind-solar system, handle the uncertainty, minimize the cost, and operate as off-grid. All of these purposes are achieved by optimal dispatching and adjusting of the loads through the proposed demand response program.

An Economic Analysis of Demand Side Management Considering Interruptible Load and Renewable Energy Integration: A Case Study of Freetown Sierra Leone

Like in most developing countries, meeting the load demand and reduction in transmission grid bottlenecks remains a significant challenge for the power sector in Sierra Leone. In recent years, research attention has shifted to demand response (DR) programs geared towards improving the supply availability and quality of energy markets in developed countries. However, very few studies have discussed the implementation of suitable DR programs for developing countries, especially when utilizing renewable energy (RE) resources. In this paper, using the Freetown’s peak load demand data and the price elasticity concept, the interruptible demand response (DR) program has been considered for maximum demand index (MDI) customers. Economic analysis of the energy consumption, customer incentives, benefits, penalties and the impact on the load demand are analyzed, with optimally designed energy management for grid-integrated battery energy storage system (BESS) and photovoltaic (PV)-hybrid system using the genetic algorithm (GA). Five scenarios are considered to confirm the effectiveness and robustness of the proposed scheme. The results show the economic superiority of the proposed DR program’s approach for both customers and supplier benefits. Moreover, RE inclusion proved to be a practical approach over the project lifespan, compared to the diesel generation alternative.

Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables

Microgrids have the potential to withstand the power outages due to their ability of islanding and potential to sustain the penetration of renewables. Increased penetration of renewables can be beneficial but it may result in curtailment of renewables during peak generation intervals due to the limited availability of storage capacity while shedding loads during peak load intervals. This problem can be solved by adjusting the load profiles, i.e., demand response (DR) programs. In contrast to the existing studies, where DR is triggered by market price signals, a local resource-triggered survivability-oriented demand response program is proposed in this paper. The proposed DR program is triggered by renewable and load level of the microgrid with an objective to minimize the load shedding and curtailment of renewables. The uncertainties in load and renewables are realized via a robust optimization method and the worst-case scenario is considered. The performance of the proposed method is compared with two conventional operation cases, i.e., independent operation case and interconnected operation case without DR. In addition, the impact of renewable penetration level, amount of shiftable load, and load absorption capacity on the performance of the proposed method are also analyzed. Simulation results have proved the proposed method is capable of reducing load shedding, renewable curtailment, and operation cost of the network during emergencies.

Demand response program using incentive and dis-incentive based scheme

Demand Response (DR) is becoming an effective tool to change consumption in order to maintain the balance between generation and consumption in real time. Demand Response Programs (DRPs) are employed for assisting the DR activities to encourage consumers to change their own load responding incentive/price based signal sent by the aggregator. Motivating the customers to change their consumption pattern poses a great challenge for the aggregator. In this paper, an incentive and dis-incentive based scheme has been designed for effective implementation of mandatory participation based incentive DR Program. This scheme is designed on the basis of social welfare concept calculating the expected gain of the generating utility and customers. While determining the benefit of generating utilities, this is observed that DR causes a loss for some costly units due to the reduction of the demand. The proposed scheme provides an incentive to such loss making generating utilities and responsive customers for playing their role in implementing the proposed DR program. Dis-incentive charge will then be imposed upon the non-responsive customers who are under the mandatory program but violate the agreement and still enjoy the monetary benefit in their energy bills due to price reduction. The effects on social welfare function due to different DR values show the importance of framing incentive prices for the participating utilities in DRP. Generation scheduling of 10 units is considered here to create different DR conditions and illustrate the effectiveness of the proposed scheme.

Real-time price-based demand response model for combined heat and power systems

Real-time electricity pricing could promote the adaptation of demand response programs in the presence of price volatility in smart grids. This paper proposes a real-time price-based demand response management model for heat and power consumers. In the proposed demand response program (DRP) the responsive electrical load can vary in different time intervals. In addition, total power and heat demands of the consumer are met, without any curtailment. The price uncertainty is envisaged through robust optimization for minimizing the worst-case electricity and heat demand procurement cost while flexibly adjusting the solution robustness. The proposed model can be easily embedded in the energy management system of the customer equipped with combined heat and power systems (CHPs), a power-only unit, a boiler unit and a heat buffer tank (HBT) and makes it possible to achieve a minimum cost. In this paper, the dual dependency characteristic of the heat and power in different types of CHPs has been taken into account. Furthermore, technical constraints, i.e. minimizing number of start-ups and shut-downs, ramp rate limits and minimum up/down-time limits of generation facilities are satisfied. Numerical simulations confirming the applicability and effectiveness of the proposed model are provided. According to the simulation results, there is a significant increase in the daily cost of the consumer without smart grid technology in comparison with the consumer employing the proposed real-time model.

Developing incentive demand response with commercial energy management system (CEMS) based on diffusion model, smart meters and new communication protocol

An incentive demand response program with two tools is proposed in this paper to help the customers to participate in the program. The first tool is the use of advanced metering infrastructure (AMI) with two-way communication system that carries significant implications for customer service, privacy and consumer protection policies. The proposed demand response program with AMI, based on real time signals, can be an effective method for utilities to manage system peaks by controlling customer loads with renewable sources. Widespread use of program with AMI will be supported by reliable and flexible ECHONET Lite Specification (ELS) communication protocol, as the second tool, that would encourage consumers to participate and reduce their energy use at peak times. ECHONET Lite, next-generation industrial and household level protocol system, is designed to integrate many devices and systems. Developing commercial energy management system (CEMS) based on advanced smart metering and ECHONET Lite protocol can help in getting the balance between the load and resources at the customers’ premises. CEMS can manage appliances and renewable resource devices at the consumers’ premises with the premise that the consumers should manage the power consumption by themselves to balance and also reduce the energy costs. The developed management system is based on a diffusion model for load balancing. The automated model of gaseous diffusion is applied as new load balancing model between the load and sources. The model helps getting an automatic balancing between the demand and resources at the customers’ premises. The proposed demand-response with real-time pricing and hourly pricing offers consumers choice and transparency, and promotes efficiency. The provided scheme provides tools that can assist the customers’ premises to participate in demand response programs.

Stochastic risk-constrained scheduling of smart energy hub in the presence of wind power and demand response

Energy hub represents a coupling among different energy networks and plays an undeniable role as the interface between energy producers and consumers. Therefore, energy hub creates a great opportunity to achieve more efficient and reliable energy systems. Considering rapid progress of wind energy in power system generation, it is crucial to treat with this resource as a part of future energy infrastructures. This paper attempts to develop a general stochastic optimization and modeling framework for solving the wind integrated smart energy hub (SEH) scheduling problem. The electrical and thermal loads of the energy hub have been served in presence of demand response (DR) programs. In the proposed DR program, the amount of responsive load can change during operation time slots of SEH. Stochastic programming method is used to deal with the impact of uncertainties related to wind power generation and load forecasting on the scheduling problem of SEH. The Monte Carlo sampling approach is used to generate the wind power and the customer demand scenarios. The scenario reduction method is also introduced to make this problem tractable. Furthermore, an appropriate risk measurement, the conditional value-at-risk (CVaR) methodology, is incorporated with the model to mitigate the risk of expected cost due to market price and load forecast volatilities.

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.

Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs

This paper presents a stochastic programming framework for solving the scheduling problem faced by an industrial customer with cogeneration facilities, conventional power production system, and heat only units. The power and heat demands of the customer are supplied considering demand response (DR) programs. In the proposed DR program, the responsive load can vary in different time intervals. In the paper, the heat-power dual dependency characteristic in different types of CHP units is taken into account. In addition, a heat buffer tank, with the ability of heat storage, has been incorporated in the proposed framework. The impact of the market and load uncertainties on the scheduling problem is characterized through a stochastic programming formulation. Autoregressive integrated moving average (ARIMA) technique is used to generate the electricity price and the customer demand scenarios. The daily and weekly seasonalities of demand and market prices are taken into account in the scenario generation procedure. The conditional value-at-risk (CVaR) methodology is implemented in order to limit the risk of expected profit due to market price and load forecast volatilities.

Short-term scheduling of combined heat and power generation units in the presence of demand response programs

This paper presents the short-term hourly scheduling of industrial and commercial customers with cogeneration facilities, conventional power units, and heat-only units. In order to serve the power and heat demands of the customer with minimum cost, demand response (DR) program has been implemented. In the proposed DR program total power and heat demand of customer will be supplied, without any curtailed load. Moreover, the responsive load can vary in different time intervals. In the paper, the heat-power dual dependency characteristic in different types of CHP (combined heat and power generation) units is taken into account and all technical constraints of generation units have been satisfied. In addition, a heat buffer tank, with the ability of heat storage, has been incorporated in the proposed framework. This work studies four cases in order to confirm the performance of the proposed method. The importance of applying the proposed DR program, the effect of considering the amount of transferable power constraint and the effect of heat exchange with the nearby factories in the value of expected profit have been investigated in the case studies.

Design of Emergency Demand Response Program Using Analytic Hierarchy Process

This paper presents an innovative approach to formulate a customer-oriented demand response (DR) program in the electricity market using the analytic hierarchy process (AHP). AHP could represent the mutual importance degrees of load reduction criteria from the various viewpoints of load-holders. Thus, the proposed DR program (DRP) could provide the reasonable decision-support process for load-holders, because the participation or nonparticipation is determined by the incentive payments corresponding to the specific load reduction conditions. The proposed DRP has the objective to maintain the system reliability at the emergency period, and it is designed as an incentive-based program (IBP) considering the real-time and by-directional operation. The modified IEEE 24-bus system for the case study is used to demonstrate the usefulness and applicability of the proposed approach, and the load profiles with the proposed DRP and the required incentive payments are obtained. The results show not only the enhancement to the system reliability but also the economic benefits within Pareto-improvement.