Emergency Demand Response Research Articles

Probabilistic Emergency Demand Response Planning Considering Overhead Lines Time Varying Thermal Ratings for Reliability Enhancements

Increasing operational flexibility in the face of uncertainties is an everlasting challenge for existing power networks, especially with rapid demand growth and high penetration of renewable energy resources (RES). This study proposes an innovative power network reliability evaluation framework that integrates a pre-defined demand response scheme employed under emergency conditions. This integration considers network uncertainties within a probabilistic approach to identify the operator's emergency demand response (EDR) contractual power participation and duration participation requirements. It implements an optimization technique to minimize the total costs from production, interruption, and EDR incentives, aiming to define the optimal EDR power reductions by considering customers' expected availability. The framework deploys an additional sequential Monte Carlo simulation, considering customers' load reduction availability and restoration duration constraints, as well as network topology, to design both their reduction and restoration schemes for improving network reliability. In addition, overhead lines (OHLs) are modeled by considering time-varying thermal ratings (TVTR) to increase network flexibility and address any demand response uncertainties. A set of case studies was performed by simulating realistic operating conditions of the proposed EDR model on extended versions of IEEE RTS79. The effects on the reliability performance indicate a significant reduction of approximately 35 % in the expected energy not supplied and interruption costs.

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.

Incentive based emergency demand response effectively reduces peak load during heatwave without harm to vulnerable groups

The incentive-based emergency demand response measure serves as an important regulatory tool during energy system operations. However, whether people will sacrifice comfort to respond to it during heatwave and what the effect on heat vulnerable populations will be are still unclear. A large-scale emergency demand response pilot involving 205,129 households was conducted in southwestern China during continuous extreme high temperatures in summer. We found that the incentive-based emergency demand response causes a statistically significant decline in electricity use with no additional financial burden on vulnerable groups. The electricity conservation potential of urban households was higher than that of rural households. Households with children did not respond to the emergency demand response, while the response of households with elderly individuals proved to be more positive. The repeated and frequent implementation of this policy did not result in an attenuation of the regulatory effect. This research can serve as a reference for countries with similar regulated power markets.

Optimization method of flexible response capability of power system with limited cost constraint

The flexible response capability of power system varies greatly under different conditions, so the optimization method of flexible response capability of power system is designed based on the theory of finite cost constraint. The bidding function with the monthly quotation as the core is obtained, and the demand response inhibition factor is calculated by combining the single inhibition rate; Under the influence of the length waveform, the one-dimensional feature recognition vector is obtained, the feature point Credibility under different conditions is obtained, the objective function is designed with the minimum operating cost, and the power emergency demand response model is established based on the concept of limited cost constraint; The optimization algorithm of flexible response capability of power system is designed, and the optimization method of response capability is obtained. The experimental results show that the optimization method has a good effect on the flexible response capability of power system in different time periods.

Online emergency demand response mechanism for new power system

The randomness, volatility, and intermittency of new energy pose huge challenges to the stable operation of the new power system. Therefore, it is necessary to improve the real-time and reliability of Emergency Demand Response (EDR). This paper proposes an online EDR mechanism based on dynamic pricing which is different from the existing one, which starts according to the participation of electricity consumers and the completion status of EDR to improve the response speed of the EDR mechanism. Each time the mechanism is activated, electricity consumers who have not exited are repriced, and their rewards for the current response interval are determined. The paper introduces the reliability parameter of electricity consumers to ensure that electricity consumers with high reliability have a higher probability of participating in EDR and receiving higher rewards. The mechanism has been proven to be truthful and achieved a nearly minimum total social cost based on second-level response through simulation experiments.

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.

Research on Electric Load Forecasting and User Benefit Maximization Under Demand-Side Response

In this paper, the real-time changes of demand-side response factors are accurately considered. First, CNN is combined with BiLSTM network to extract the spatio-temporal features of load data; then an attention mechanism is introduced to automatically assign the corresponding weights to the hidden layer states of BiLSTM. In the optimization part of the network parameters, the PSO algorithm is combined to obtain better model parameters. Then, considering the average reduction rate of various users under energy efficiency resources and the average load rate under load resources on the original forecast load and the original forecast load, the original load is superimposed with the response load considering demand-side resources to achieve accurate load forecast. Finally, “price-based” time-of-use tariff and “incentive-based” emergency demand response are selected to build a load response model based on the principle of maximizing customer benefits. The results show that demand-side response can reduce the frequency and magnitude of price fluctuations in the wholesale market.

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.

Scheduling Online EV Charging Demand Response via V2V Auctions and Local Generation

Due to the enormous energy consumption and the wide geographic distribution, Electrical Vehicle (EV) charging stations are believed to have great potential in Emergency Demand Response (EDR) participation. However, EDR limits the electricity drawn from the power grid by the charging station, and can pose threats to satisfying EVs’ charging demand. In this paper, in order to complement the charging station’s energy supply to meet the dynamic EV charging demand, we formulate an online EV charging scheduling problem under EDR as a non-linear mixed-integer program, and propose a novel polynomial-time online algorithm and auction mechanism to jointly incentivize EVs with energy to sell their energy and utilize the charging station’s local generator to produce energy. Our approach conducts an auction in each single round based on a primal-dual method and ties these auctions over time to optimize the system’s long-term social cost, while accommodating the local generator’ on/off-state control, each EV bidder’s cumulative energy budget constraint, and the power grid’s EDR energy cap. Our approach achieves the economic properties of truthfulness, individual rationality, and computational efficiency simultaneously for each auction, and a parameterized-constant competitive ratio for the long-term social cost. By rigorous theoretical analysis and trace-driven experimental studies, the results exhibit that our approach outperforms multiple alternative algorithms regarding the social cost, attains the economic properties, and also executes efficiently in practice.

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.

A Hybrid Adaptive Algorithm for Power System Frequency Restoration Based on Proposed Emergency Demand Side Management

The power system blackouts have occurred in recent years all across the world. Therefore, it is necessary to develop more effective methods for preventing such events. In this paper, a novel effective method is proposed to restore the frequency of power system and make the system stable for major disturbances. To do this, for the first time, the adaptive load shedding and emergency demand response algorithms are combined to improve the power quality in addition to system stability. To evaluate the proposed method, the New England 39-bus standard grid is selected and simulated using DIgSILENT software package and its performance is studied for moderate and large power deficit scenarios. The obtained results prove the superiority of the new method in comparison with the pervious methods from points of view lower power interruption and reducing the probability of power system collapse by decreasing the convergence time and minimizing the frequency drop.