Emergency Demand Response Program Research Articles

Residential electricity demand on CAISO Flex Alert days: a case study of voluntary emergency demand response programs

Abstract The California Independent System Operator (CAISO) utilizes a system-wide, voluntary demand response (DR) tool, called the Flex Alert program, designed to reduce energy usage during peak hours, particularly on hot summer afternoons when surges in electricity demand threaten to exceed available generation resources. However, the few analyses on the efficacy of CAISO Flex Alerts have produced inconsistent results and do not investigate how participation varies across sectors, regions, population demographics, or time. Evaluating the efficacy of DR tools is difficult as there is no ground truth in terms of what demand would have been in the absence of the DR event. Thus, we first define two metrics that to evaluate how responsive customers were to Flex Alerts, including the Flex Period Response, which estimates how much demand was shifted away from the Flex Alert period, and the Ramping Response, which estimates changes in demand during the first hour of the Flex Alert period. We then analyze the hourly load response of the residential sector, based on ∼200 000 unique homes, on 17 Flex Alert days during the period spanning 2015–2020 across the Southern California Edison (SCE) utility’s territory and compare it to total SCE load. We find that the Flex Period Response varied across Flex Alert days for both the residential (−18% to +3%) and total SCE load (−7% to +4%) and is more dependent on but less correlated with temperature for the residential load than total SCE load. We also find that responsiveness varied across subpopulations (e.g. high-income, high-demand customers are more responsive) and census tracts, implying that some households have more load flexibility during Flex Alerts than others. The variability in customer engagement suggests that customer participation in this type of program is not reliable, particularly on extreme heat days, highlighting a shortcoming in unincentivized, voluntary DR programs.

Optimal resilient microgrids formation based on darts game theory approach and emergency demand response program for cyber-physical distribution networks considering natural disasters

In this paper, novel multi-objective microgrids (MGs) formation method-based darts game theory optimization algorithm is proposed. The main objectives are to minimize the total losses, amount of load-shedding, and total restoration cost while ensuring a variety of topological and electrical constraints. The network graph theory represents the formed MG, and the non-linear equations of power flow and loss calculations are adopted in the MG formation model. In the proposed model, the DRERs include diesel generators, fuel cells, micro-turbines, and renewables wind and PV units with energy storage systems. The demand response program includes curtailable loads to improve load profile reduction during peaks. To assess the system's resiliency under extremely inevitable incidents, resiliency metrics are applied. To validate the effectiveness of the proposed approach, the modified IEEE 33-bus test system is utilized. Single-fault and multiple faults disaster case studies are performed with and without considering the emergency demand response program (EDRP). The results show the effect of EDRP in minimizing system losses, load-shedding, restoration cost, and improving the resiliency metrics after natural disasters. Finally, the analytic hierarchy process (AHP) model indicates the most efficient operation scenario while achieving the system objectives considering the problem alternatives.

Impact of implementing emergency demand response program and tie-line on cyber-physical distribution network resiliency

Recently, due to the complex nature of cyber-physical distribution networks (DNs) and the severity of power outages caused by natural disasters, microgrid (MG) formation, distributed renewable energy resources (DRERs), and demand response programs (DRP) have been employed to enhance the resiliency of these networks. This paper proposes a novel multi-objective MGs formation method-based darts game theory optimization algorithm. The microgrid formation is obtained by controlling the sectionalizing and tie-line switches. The network graph theory is used to represent the constructed microgrid, and the non-linear equations of power flow and loss calculations are adopted in the microgrid formation model. To measure the system's resiliency under extreme disaster events, metrics are utilized to prove the system's flexibility and resiliency. The modified IEEE 33-bus test system is designed to validate the proposed approach's effectiveness. Three case studies are performed with and without considering the emergency demand response program (EDRP) and tie-lines.

Improvement of regional market management considering reserve, information-gap decision theory, and emergency demand response program

There is a variety of items which should be taken into consideration by a regional market manager (RMM). Participants in the market, technical constraint, price variation/reaction, electricity-price uncertainty and types of the applied demand response program are some instances in this regard. One of the demand response programs is Emergency Demand Response Program (EDRP) which is considered in this paper. In the present study, the objective function of the RMM is formulated in a market environment in order to determine the optimal demand, incentive and power purchased with considering some of technical constraints such as incentive limits, demand limits, power purchased and power balance. Co-evolutionary Improved Teaching Learning-Based Optimization (C-ITLBO) is applied to maximize the RMM’s profit. Furthermore, determination of the demand level in the EDRP is performed on the basis of a logarithmic model which includes the price elasticity matrix (PEM) is included. The reserve supplied due to Aggregators (AGGs) is also prioritized using the reserve-margin factor (RMF). In addition, information-gap decision theory (IGDT) is applied to model uncertainty in the initial electricity price. The above mentioned items are modeled in a multi-level formulation.

Constructing a (fair) load-reduction emergency demand response program: A fee-and-rebate pricing mechanism

Emergent power shortages and power outages cause enormous economic losses. Special fees and rebates at times when the operating reserve is below standard are among the most popular mechanisms for driving demand response, i.e., a spontaneous reduction in power usage, to help alleviate peak loads. However, a rebate which is uniform for all users usually under- or over-estimates companies’ incentive to respond, as product profitability and the cost of manufacturing vary from one company to another. As a result, the power provider either fails to sufficiently reduce the load during a period of scarcity of electric power or unduly sacrifices profits to pay an excessive rebate. This study presents a Stackelberg game-theoretic model that determines discriminative rebates and scarcity pricing of electricity, thereby capturing incentives of different industrial users to respond rationally and reduce their power usage. The model can be used to assess the current pricing and rebate structure and has the potential to serve as the fee-and-rebate mechanism in an Emergency Demand Response Program. A case study is used to simulate a game between the Taiwan Power Company and four classes of heterogeneous industrial customers who are willing to cooperate with the power provider on implementation of an Emergency Demand Response Program. The results show that the profits of the participants are higher than the profits of the non-participants. Moreover, a high-value-added and non-interruptible manufacturing class should be paid higher rebate.

Field experimental evidence of how social relations shape behavior that promotes energy conservation

Energy demand-side management is essential for deep decarbonization. However, while we target people as discrete and isolated individuals, we ignore the fact that energy consumption occurs in intricate webs of pre-existing social relations. This study examines an emergency demand response (EDR) program in China involving more than 180,000 households based on relations with family members, communities, and identity. The results indicate that the existence ofsocial relations can promote energy conservation behavior and there is a large degree of heterogeneity. Targeted relations, such as single-living residents, small-scale households, city dweller etc., show greater electricity savings with 12.35% increase in overall effect, which benefit when demographics change in the next few decades. Such heterogeneous changes put forward the pressing need for policymakers to focus on social relations as a unit in future intervention designs to decarbonize the energy system.

Impact of demand response on battery energy storage degradation using gbest-guided artificial bee colony algorithm with forecasted solar insolation

The role of energy storage technology is gaining momentum as prosumers are actively participating in the retail electricity market. For the local energy community equipped with a grid-tied rooftop photovoltaic (PV) system, battery energy storage (BES) is a vital element to overcome the reliability issues occurring due to intermittency in renewable energy sources (RES). The PV-BES combination at the residential level is quite challenging as it involves optimal sizing and economical operation of the system. Also, rechargeable batteries are prone to aging and are majorly affected by temperature, state-of-charge (SOC), and charge/discharge rates (Crate), resulting in degradation and shortened useful life of BES. To this end, a single-objective optimization for minimizing the total degradation cost of BES is presented. In the first phase of this work, the day-ahead solar insolation is forecasted with an outlier filter-based autoencoder long short-term memory (LSTM-AE) for enhanced prediction accuracy. The mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R) of the LSTM-AE model are found to be 0.0423, −0.0117, and 0.984 respectively. The emergency demand response program (EDRP) is modeled in the second phase. An incentive of 20 ₵/kWh is found to balance the service provider (SP) revenue and prosumer benefit (PB) for prosumer participation in EDRP. Furthermore, the degradation cost of BES with an initial status of 20%, 55%, and 95% of SOC is optimized using a tuned gbest-guided artificial bee colony (GABC) algorithm both in the absence and presence of EDRP. The optimal solution obtained with EDRP shows daily savings, with three initial status of SOC, to be 18.78%, 12.14%, and 11.18% for summer, 16.15%, 13.77%, and 8.88% for mild, and 20%, 15.65%, and 12.16% for winter respectively, when compared without EDRP. Hence, this study demonstrates the merits of planning and prosumer participation in the retail electricity market and has a potential application in the real local energy community. The grid-connected PV-BES residential system is implemented in Python-Jupyter Notebook and MATLAB.

Flexibility-based generation maintenance scheduling in presence of uncertain wind power plants forecasted by deep learning considering demand response programs portfolio

The special characteristics of renewable energy resources, such as non-emission and low operating costs, have increased the penetration rate of renewable energy resources in the power system. However, the variability nature of renewable energy resources has led to some challenges in power system studies. Therefore, system flexibility has been taken into account, which plays a key role in dealing with renewable energy resources uncertainty. On the other hand, generation maintenance scheduling is one of the most important and effective programs for short-term studies of the power system. It seems necessary to implement a flexibility-based generation maintenance scheduling in order to achieve more flexible operation as a consequence of providing flexible resources. Here, a novel framework for flexibility-based multi-objective generation maintenance scheduling associated with a portfolio of demand response programs is introduced. Hence, a system flexibility index has been applied in this paper. Moreover, a portfolio of multifarious demand response programs as a negawatt resource has been applied as a provider of flexibility from demand-side point of view. In this paper, direct load control, real-time pricing, and emergency demand response programs are implemented as flexibility providers through comprehensive modelling of DRPs. Herein, proper modeling and forecasting of renewable energy resources production will increase the scheduling accuracy. Hence, the uncertainty of wind power plants is considered by deep learning methodology in Python. Reducing costs such as operation and maintenance costs, leveling the reserve margin and increasing flexibility index are regarded as multifarious objectives of flexibility-based generation maintenance scheduling. Due to the good handling of large-scale, non-convex and non-proportional objective functions, the augmented epsilon constraint method is utilized to evaluate flexibility-based multi-objective generation maintenance scheduling. According to the results, the system flexibility has been improved without increasing costs. Several analyses are carried out on a modified IEEE-RTS 24 bus to trace the capability of the proposed structure.

Impact of Local Emergency Demand Response Programs on the Operation of Electricity and Gas Systems

With increasing attention to climate change, the penetration level of renewable energy sources (RES) in the electricity network is increasing. Due to the intermittency of RES, gas-fired power plants could play a significant role in backing up the RES in order to maintain the supply–demand balance. As a result, the interaction between gas and power networks are significantly increasing. On the other hand, due to the increase in peak demand (e.g., electrification of heat), network operators are willing to execute demand response programs (DRPs) to improve congestion management and reduce costs. In this context, modeling and optimal implementation of DRPs in proportion to the demand is one of the main issues for gas and power network operators. In this paper, an emergency demand response program (EDRP) is implemented locally to reduce the congestion of transmission lines and gas pipelines more efficiently. Additionally, the effects of optimal implementation of local emergency demand response program (LEDRP) in gas and power networks using linear and non-linear economic models (power, exponential and logarithmic) for EDRP in terms of cost and line congestion and risk of unserved demand are investigated. The most reliable demand response model is the approach that has the least difference between the estimated demand and the actual demand. Furthermore, the role of the LEDRP in the case of hydrogen injection instead of natural gas in the gas infrastructure is investigated. The optimal incentives for each bus or node are determined based on the power transfer distribution factor, gas transfer distribution factor, available electricity or gas transmission capability, and combination of unit commitment with the LEDRP in the integrated operation of these networks. According to the results, implementing the LEDRP in gas and power networks reduces the total operation cost up to 11% and could facilitate hydrogen injection to the network. The proposed hybrid model is implemented on a 24-bus IEEE electricity network and a 15-bus gas network to quantify the role and value of different LEDRP models.

Fairness algorithm for emergency demand response operation in distribution networks

As a response to increased power demand, electrical utilities have started to use Demand Response (DR) programs to improve the stability, efficiency and reliability of their networks. One type of DR, an Emergency Demand Response Program (EDRP), addresses stability challenges by sending curtailment orders to consumers associated with the program. There are different methods for calculating the amount of power that should be curtailed and how it should be divided among consumers. An important aspect of curtailment assignment is fairness, yet there is no agreed-upon manner of achieving fairness in curtailment allocation. This paper presents a new fairness-based EDRP for a residential area. The fairness is manifested in the algorithm by taking into account the number of times and the amount of energy a customer is asked to curtail for a predetermined period of time. The proposed algorithm is demonstrated and verified using simulations.

A Novel Model of Emergency Demand Response Program for Optimal Aggregators’ Strategy

Demand response (DR) programs have been used for a variety of purposes. In this study, Emergency Demand Response Program (EDRP), as an incentive-based program, is used for peak clipping, managing the volatility and the increase of the electricity prices, monetizing consumers’ involuntary electricity outage and controlling the price in the day-ahead market. A market-driven model in the form of a combination of Diamond’s over-lapping generations (OLG) and Price Elasticity (PE) formulations is proposed for the aggregates (AGGs) by which they can manage their consumers’ demand at the peak hours and maximize the profit. Furthermore, maximizing the profit of the AGGs was performed by determining the optimal amount of demand while three technical constraints such as demand ceiling, relative risk aversion and incentive limits were also included in the proposed model. The profit function of the AGGs is considered in the form of the difference between the revenue and the cost. The problem is solved with taking into consideration the different rate of time preference and PE over a 24-hour period. The simulation results showed a successful performance of the proposed model from the considered economical and technical viewpoints.

Optimal price-based and emergency demand response programs considering consumers preferences

This paper presents a pricing optimisation framework for energy, reserve, and load scheduling of a power system considering demand response (DR). The proposed scheduling framework is formulated as a reliability-constrained unit commitment program to minimise the power system operation costs by finding optimal electricity prices and optimal incentives while guaranteeing the reliability of the system during contingencies. Moreover, customers’ attitude toward the electricity price and incentive adjustment and the effect of their preferences on load scheduling and operation of the system are investigated in various DR programs. The proposed scheme is implemented on an IEEE test system, and the scheduling process with and without DR implementation is discussed in detail by a numerical study. The proposed method helps both the system operators and customers to reliably schedule generation and consumption units and select the proper DR program according to defined prices and incentives in the case of an emergency.

Online Edge Computing Demand Response Via Deadline-Aware V2G Discharging Auctions

Distributed edge computing systems that participate in Emergency Demand Response (EDR) programs can adjust workload across heterogenous edges to reduce total energy consumption. Unfortunately, this approach may not always reduce sufficient energy as required by EDR. In this paper, we propose to leverage Electrical Vehicles (EVs) and Vehicle-to-Grid (V2G) techniques to provide energy to the edge system, and design an auction mechanism to incentivize EVs to discharge energy for the edges. Yet, we face critical challenges, such as the uncertainty of EV bid arrivals, the restriction of discharging deadlines, and the desire to achieve required economic efficiency. To overcome such challenges, we design a novel online approach, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$E^{3}$</tex-math></inline-formula> DR, of multiple algorithms that decompose our original NP-hard social cost minimization problem into two subproblems, solve the first subproblem via reformulation, the primal-dual optimization theory, and a careful payment design, and solve the second subproblem via standard solvers. We have rigorously proved that our approach finishes in polynomial time, achieves truthfulness and individual rationality economically, and leads to a parameterized competitive ratio for the long-term social cost. Through extensive evaluations using real-world data traces, we have validated the superior practical performance of our approach compared to existing algorithms.

Effectiveness of demand response in achieving supply-demand matching in a renewables dominated electricity system: A modelling approach

Globally, electricity systems are undergoing transitions from robust, carbon-intensive, and firm power conventional systems to uncertain, intermittent, and variable renewable energy integrated low carbon systems. These transitioning electricity systems have moved from a situation of “matching available supply with dynamic demand” to “matching dynamic supply with dynamic demand.” These transformations have led to several new challenges - significant mismatch in periods of high supply and high demand, the shift in the method of accessing energy resources for electricity generation from “procure, store and generate when needed” to “generate when available,” continuous struggle to match variable supply with variable demand, and installed capacity redundancy, temporal as well as permanent leading to low plant load factors. Actions on the supply-side alone will not be enough to address these challenges and achieve optimal functioning of the electricity system. We need effective demand-side solutions, too, to manage variations in both supply and demand. In this paper, it is proposed to study the effectiveness of demand-side interventions as potential solutions for managing the variabilities introduced by renewable energy mainstreaming. Towards this, we develop a mixed-integer linear programming model to implement and validate emergency and economic demand response (DR) programs. DR options like load curtailment, short-, medium- and long-term load shifting are considered with both incentive-based and penalty-based pricing strategies to influence consumer participation. The Karnataka electricity system is used as a case study for model implementation and validation. The findings suggest that DR interventions are very effective in moderating variability in electricity demand by chopping the peak loads and topping the valleys. Further, benefits include postponement of installed capacity additions, enhanced utilization of available capacity, and minimization of demand variability.

Modelling Demand Response to Alleviate Congestion in Active Power Market

A new method of congestion management in deregulated and competitive power system based on a combination of Demand Response (DR) program and generation re-dispatch is proposed in this research. One of DR program called Emergency Demand Response Program (EDRP) is carried out through customer's willingness to participate in this program in order to reduce their consumption during congestion. EDRP is modeled based on demand elasticity of the load and considering incentives. Different level of demand elasticity values is introduced to the customers to observe their contribution in congestion relief. The proposed method is examined on IEEE 30 bus system by using the Optimal Power Flow tool and it indicates that by integrating the customer’s elasticity for EDRP can decrease the cost to relieve the congestion and lead more benefit for all participants. The obtained results are the cost to manage congestion problem and optimal re-dispatch of generators by involving the participation of customers in EDRP.

Improving energy efficiency in colocation data centers for demand response

Colocation data centers (colocations, for short) are developing rapidly and have become ideal participants for emergency demand response (EDR) programs. However, even colocation operators wish to save energy, they cannot achieve energy reduction without coordination from the tenants, because the servers in the colocations are owned and operated by the tenants. To solve the “uncoordinated relationship” issue between operators and tenants, some works have been done by way of incentivizing the tenants to reduce the energy consumption of their servers. However, apart from servers, the power consumption of the cooling system also accounts for a large portion in a colocation, which should be optimized as well. Unfortunately, the servers and the cooling system are controlled by tenants and operators separately in colocations, and thus “uncoordinated relationship” issue also exists between IT and cooling systems. In this paper, coarse-grained and fine-grained incentive mechanisms are proposed to solve these problems. Approximation algorithms are developed to optimize the energy-saving problems. Furthermore, Vickrey–Clarke–Groves (VCG) theory is introduced into our incentive mechanism design to guarantee the feasibility and truthfulness of the two mechanisms. Trace-driven simulations are performed to validate the effectiveness of the two incentive mechanisms. The results show that compared with the existing incentive mechanisms, up to 20.50% of the energy-saving cost can be reduced in the coarse-grained mechanism and 28.34% of the cost reduction can be achieved in the fine-grained mechanism.

Optimal Outage Management Model Considering Emergency Demand Response Programs for a Smart Distribution System

In this paper, a novel smart outage management system considering Emergency Demand Response Programs (EDRPs) and Distributed Generations (DGs) denoted as SOMSDGsEDRPs is proposed. The EDRPs are provided to decrease the cost of load shading in a time of emergency. The objective function of the problem is proposed to minimize the load shading cost, the DG dispatch cost, the demand response cost and the repair dispatch time for crews. The SOMSDGsDERPs solves an optimization problem that is formulated as Mixed Integer Linear Programming (MILP) taking into account the grid topology constraints, EDRP constraints, DG constraints and crew constraints. The MILP formulation was demonstrated in the GAMS software and solved with the CPLEX solver. The proposed method was tested on the IEEE 34 bus test system as well as an actual Iranian 66 bus power distribution feeder. The results show that the EDRPs and DGs can be effective in decreasing the outage cost and increasing the served load of the distribution power system in a crisis time.

Analysis of Emergency Demand Response Levels of Central Air-Conditioning

Central air-conditioning (CAC) is a flexible load which can be dispatched during an emergency demand response (EDR) program. However, consumers’ trade-off between thermal comfort levels (TCLs) and profits will affect the potential of CAC. Therefore, this paper proposes a method to evaluate CAC’S response levels. An EDR optimization model simulating consumers’ trade-off between TCLs and profits is established and it is solved by a particle swarm optimization (PSO) algorithm. Then the definition of EDR levels is presented to quantitatively analyze the capability of CAC providing the EDR. Furthermore, uncertainty of EDR levels caused by stochastic initial indoor temperature is analyzed through a Monte Carlo (MC) simulation. A case study shows the rationality of the presented method, and the effects of weight coefficients, compensation prices and penalty prices on EDR levels are also analyzed.

Multi-objective optimal power flow considering voltage stability index and emergency demand response program

In this paper, optimal power flow problem has been implemented and evaluated simultaneously with the emergency demand response program (EDRP), considering the economic and environmental aspects, as well as the voltage stability enhancement of power system. Moreover, employing DRPs is a way to control customers’ electric energy consumption and to improve network reliability. The EDRP is one of the various demand response programs that provides incentives for participants in the program to reduce their consumption during peak hours or shift to off-peak hours. In order to solve the multi-objective function and achieve optimal compromise, the multi-objective Non-Dominated Sorting Genetic Algorithm II was used in this study. The proposed model was implemented on a 30-bus IEEE system. The results show that the proposed model is very effective in reducing fuel costs, emission and improving voltage stability.

Real-time emergency demand response strategy for optimal load dispatch of heat and power micro-grids

Heat and power micro-grids are of great significance in improving the flexibility, efficiency, and reliability of the energy system. Load dispatch of micro-grids considering the emergency demand response (EDR) program is an important optimization problem, which requires the consumers to respond to the emergency load reduction signals in real-time. In this regard, an optimal load dispatch strategy of heat and power micro-grids is proposed to respond to the EDR signals without compromising customers’ production process. The strategy encompasses two stages, the rolling optimization stage (ROS) and the real-time emergency demand response stage (REDRS). The tow-stage optimization model is proposed to address the coordination problems brought by the EDR events. The ROS integrated with the model predictive control (MPC) framework is to alleviate the negative effects due to the deviation between the forecasting and real-time data. REDRS is to generate a real-time load reduction plan in response to the EDR events. The proposed strategy makes the efforts to achieve the economic and environmental dispatch of micro-grids with both heat and power demand satisfied. In this study, four cases are discussed to verify the performance of the two-stage strategy. The simulation results show that the total cost and purchased electricity can be effectively reduced through participating EDR programs. It can also be seen from the numerical simulations that the large consumer could gain 23.51–41.83% of the electricity reduction which is purchased from the grid and decrease 11.04–13.28% of total cost in each EDR interval. Besides, the short and long term simulations reveal that the utility company could also achieve the reduction of peak load which could be seen as a “win-win” strategy.