Demand Response Participation Research Articles

The Research on System of Charging Load Aggregator Participating in Market Transaction based on Internet of Vehicles

The electric vehicle market has gained rapid growth in various countries. Electric vehicles will be one of the largest loads in the future power grid. This paper sorts out the typical models of charging load aggregators’ participation in ancillary services, demand response, and valley drop electricity curve tracing, and formulates the trading strategies of charging load aggregators in the electricity direct trading market, ancillary service market and electricity derivatives market. And then the strategy of aggregators participating in multi market coordination is proposed. Based on the contribution of aggregators to participate in market transactions, considering the willingness of different entities to participate in charging load aggregated transactions, a reasonable game model as well as a fair and reasonable value sharing mechanism is established to achieve the transmission of market trading dividends and promote the charging load aggregators to participate in the electricity market transactions.

The use of dynamic programming and golden section search for the optimal load‐shedding strategy of HEMS participating in demand response program

This paper presents the optimal load-shedding strategy of home energy management systems (HEMSs). The HEMS uses the dynamic programming (DP) method to plan the day ahead, schedule, and connects to smart plugs to perform load-shedding. HEMS solves the shedding schedule of smart plugs considering the prediction of consumption of each smart plug and the consumer's demand response (DR) setting. In addition, the maximum DR capacity was determined by using golden section search (GSS). Some DR experiments were conducted by the proposed method in laboratory and these comprised three different DR durations. The results show that the estimated DR capacities and shedding schedules were feasible. Thus, it helps both the DR participants and aggregator to seize the optimum performance and benefits.

Performance of Existing Methods in Baselining Demand Response From Commercial Building HVAC Fans

Abstract Commercial buildings generally have large thermal inertia and thus can provide services to power grids (e.g., demand response (DR)) by modulating their heating, ventilation, and air conditioning (HVAC) systems. Shifting consumption on timescales of minutes to an hour can be accomplished through temperature setpoint adjustments that affect HVAC fan consumption. Estimating the counterfactual baseline power consumption of HVAC fans is challenging but is critical for assessing the capacity and participation of DR from HVAC fans in grid-interactive efficient buildings (GEBs). DR baseline methods have been developed for whole-building power profiles. This study evaluates those methods on total HVAC fan power profiles, which have different characteristics than whole-building power profiles. Specifically, we assess averaging methods (e.g., Y-day average, HighXofY, and MidXofY, with and without additive adjustments), which are the most commonly used in practice, and a least squares-based linear interpolation method recently developed for baselining HVAC fan power. We use empirical submetering data from HVAC fans in three University of Michigan buildings in our assessment. We find that the linear interpolation method has a low bias and by far the highest accuracy, indicating that it is potentially the most effective existing baseline method for quantifying the effects of short-term load shifting of HVAC fans. Overall, our results provide new insights on the applicability of existing DR baseline methods to baselining fan power and enable the more widespread contribution of GEBs to DR and other grid services.

Automated Control of Transactive HVACs in Energy Distribution Systems

Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building's energy consumption. In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. In this article, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user comfort simultaneously. Accordingly, we develop the control strategy through the solution of an optimization problem that balances between the energy cost and consumer's dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control strategy for HVAC systems that balances between electricity cost and user comfort.

Day-ahead scheduling of energy hubs with parking lots for electric vehicles considering uncertainties

Energy hubs (EHs) are units in which multiple energy carriers are converted, conditioned and stored to simultaneously supply different forms of energy demands. In this research, the objective is to develop a new stochastic model for unit commitment in EHs including an intelligent electric vehicle (EV) parking lot, boiler, photovoltaic (PV) module, fuel cell, absorption chiller, electric heat pump, electric/thermal/cooling storage systems, with electricity and natural gas (NG) as inputs and electricity, heat, cooling and NG as demands. The uncertainties of demands, PV power and initial energy of EV batteries are modeled with Monte Carlo Simulation. The effect of demand response and demand participation factors as well as effect of EVs and storage systems on EH operation are investigated. The results indicate that thermal demand response is more effective than electric and cooling demand response; as it decreases EH operation cost by 12%, while electric demand response and cooling demand response decrease it respectively by 9.3% and 4.2%. The results show that at low electric/thermal/cooling demand participation factors, an increase in participation factor sharply decreases EH operation cost, while the same amount of increase at higher participation factors leads to a smaller decrease in operation cost. The results also indicate that thermal storage system and cooling storage system have significant effect on reduction of EH operation cost, while the effect of electric storage system is trivial.

Data-driven modelling of energy demand response behaviour based on a large-scale residential trial

Recent years have seen an increasing interest in Demand Response (DR), as a means to satisfy the growing flexibility needs of modern power grids. This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix, and increasing complexity in demand profiles from the electrification of transport networks. Currently, less than 2% of the global potential for demand-side flexibility is currently utilised, but a more widespread adoption of residential consumers as flexibility resources can lead to substantially higher utilisation of the demand-side flexibility potential. In order to achieve this target, acquiring a better understanding of how residential DR participants respond in DR events is essential – and recent advances in novel machine learning and statistical AI provide promising tools to address this challenge. This study provides an in-depth analysis of how residential customers have responded in incentive-based DR, utilising household-related data from a large-scale, real-world trial: the Smart Grid, Smart City (SGSC) project. Using a number of different machine learning approaches, we model the relationship between a household’s response and household-related features. Moreover, we examine the potential effects of households’ features on the residential response behaviour, and highlight a number of key insights which raise questions about the reported level of consumers’ engagement in DR schemes, and the motivation for different customers’ response level. Finally, we explore the temporal structure of the response – and although we found no supporting evidence of DR responders learning over time for the available data from this trial, the proposed methodologies could be used for longer-term longitudinal DR studies. Our study concludes with a broader discussion of our findings and potential paths for future research in this emerging area.

A Modified Intra-Day Market to Trade Updated Forecast Information for Wind Power Integration

The market illiquidity and inflexible demand response (DR) participation are within the main restrictions in today's existing intra-day (ID) market. This paper proposes a modified ID market which enables direct transactions of updated forecast information regarding power deviations instead of trading deviated power. The modification brings about no significant challenges for participants as suppliers still submit offers revealing the quantity of power deviations despite the deviated power would be settled in real-time (RT) market, while purchasers decide on the optimal behaviors in RT market based on their own judgment on RT market prices with the purchased information instead of directly offsetting the deviated power of suppliers. The flexibility of DR participation could be promoted since DR providers as purchasers no longer have to bid when the ID offers arrive, because the information sold to others would not be removed from the order book and could be purchased by multiple participants. As a result, the modified ID market attracts more participants, and the more purchasers there are, the lower costs each purchaser will pay, thus the market liquidity could be improved. Finally, only effective information can be paid to suppliers so they are restrained from untruthfully revealing the actual forecast when submitting offers and improvement on wind power forecast accuracy can be encouraged. Case studies further illustrate the feasibility and efficiency of the modified ID market.

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.

Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating

Applications of electric heating, which can improve carbon emission reduction and renewable energy utilization, have brought new challenges to the safe operation of energy systems around the world. Regenerative electric heating with load aggregators and demand response is an effective means to mitigate the wind curtailment and grid operational risks caused by electric heating. However, there is still a lack of models related to demand response, which results in participants not being able to obtain maximum benefits through dynamic subsidy prices. This study uses the Weber–Fechner law and a clustering algorithm to construct quantitative response characteristics models. The deep Q network was used to build a dynamic subsidy price generation framework for load aggregators. Through simulation analysis based on the evolutionary game model of a project in a rural area in Tianjin, China, the following conclusions were drawn: compared with the benchmark model, regenerative electric heating users can save up to 8.7% of costs, power grid companies can save 56.6% of their investment, and wind power plants can increase wind power consumption by 17.6%. The framework proposed in this study considers user behavior quantification of demand response participants and the differences among users. Therefore, the framework can provide a more reasonable, applicable, and intelligent system for regenerative electric heating.

Strategic Participation of Residential Thermal Demand Response in Energy and Capacity Markets

The value proposition of residential thermal demand response (DR) could be enhanced through participation in multiple electricity markets. However, this potential value could be affected by misalignment of the strategic objectives of DR participants and also by the adoption levels of flexible heating technologies. This article presents an extended multi-perspective model (EMPM) which integrates the optimization problems of the DR participants within a single framework to evaluate the participation of residential thermal DR in energy and capacity markets. The capacity market participation is explicitly modeled based on parameters obtained using a bespoke capacity value assessment methodology. Subsequently, an iterative algorithm is proposed for determining consumer adoption levels of flexible heating technologies. The results indicate that while capacity market participation of residential thermal DR would be beneficial for all DR entities, consumers' comfort preferences would have a noticeable impact on the results. Additionally, very high consumer adoption levels of flexible technologies would reduce power system costs and increase investment in low-carbon generation, but would not be optimal from the consumers' investment perspective. Additionally, sensitivity analyses highlight that multiple market participation, energy inefficiency of the building fabric and greater preference for comfort could incentivize greater consumer adoption of flexible heating technologies.

Stochastic Optimization for Residential Demand Response With Unit Commitment and Time of Use

As compensation to power generation dispatch, demand response (DR) enables demand controllability by changing the consumers' electricity usage patterns, which can be used to reduce electricity cost, integrate renewable energy, and provide ancillary services. To reveal the benefits from residential DR, this study develops the following two approaches: optimal load aggregation under augmented time-of-use (TOU) pricing; and active DR participation in unit commitment (UC) under rewards. We have shown that plain TOU pricing is not a promising DR policy if residential customers are equipped with home energy management systems (EMSs). We, therefore, propose an augmented TOU by radial basis functions. With a 60% participation level, the proposed optimal load aggregation model under the augmented TOU can reduce the power generation cost by 24% and decrease the standard deviation of the load profile by 42%. However, these results can be affected by the customer's participation level, which is also quantitatively studied. Specifically, when the participation level exceeds 80%, this method becomes less efficient. The second proposed approach, a two-stage stochastic UC model with DR flexibility, reduces the power generation cost by 20% and decreases the standard deviation of the load profile by 77%. In addition, the inconvenience of DR participation is quantitatively evaluated, and a Pareto surface is developed, which can be used as a baseline for residential customers to set up the home EMS for DR implementation. Both the proposed mechanisms can be used to improve the energy efficiency by uncovering the residential DR potential.

Robust capacity optimization methods for integrated energy systems considering demand response and thermal comfort

Integrated Energy System can realize the cascade utilization of energy, which improves the utilization of energy efficiently and reduce the carbon emission. Taking into account the uncertainty of multi-energy load and renewable energy forecasting, this paper presents a bi-level robust optimization model with demand response and thermal comfort, for the capacity planning and operation problem of Integrated Energy System. The outer level optimization is planning to find the optimal integrated energy system configuration to minimize economic investment, while the inner level is to robustly optimize the system scheduling to simultaneously reduce carbon emissions and dissatisfaction of residents’ participation in demand response. In the simulation, the Pareto front of the multi-objective problem is obtained via NSGA-II algorithm and Gurobi solver, and the best design plans on the Pareto front selected by Topsis method are analyzed and discussed. The results from three modes are compared in simulation, which illustrates the economic and environmental benefits from demand response and thermal comfort. In addition, the impact of different thermal time scales and forecast uncertainties on integrated energy system planning are also discussed. Finally the sensitivity of the influence of the low carbon grid constrains on the optimization is analyzed.

Aggregation Optimization Method of Virtual Energy Storage for Electric Vehicles Considering User Elasticity

With the continuous development of electric vehicle charging facilities, the impact of electric vehicles on the power grid is growing. Considering the automatic demand response technology of smart grid, charging pile operators participate in the demand response plan and guide users to charge according to the price signal or incentive mechanism, which can ensure the safety of the power grid and reduce the negative effects of electric vehicles on the grid influence. Based on the theory of demand side response elasticity, this paper analyzes the whole process of electric vehicle users' participation in demand response and its influencing factors, classifies electric vehicle users based on demand response, and then quantitatively analyzes users' demand response from the perspective of electricity price elasticity and consumption psychology. Finally, the demand side response model with user participation is established. The application of the model can effectively improve the enthusiasm of electric vehicle users to participate in demand side response, and reduce the negative impact of electric vehicles on the power grid.

Optimal Day-Ahead Decision-Making Scheduling of Multiple Interruptible Load Schemes for Retailer With Price Uncertainties

The interruptible load contract is a flexible and reliable resource that can help retailers reduce market risk in the form of an option contract. However, the mechanism of IL contract price formation and the representation of value are rarely considered in the study of interruptible load contracts, which helps reduce the contract price. It is studied in the paper that the optimal decisions by retailer in the day-ahead spot market under multiple types of interruptible load contracts. Four IL contracts are proposed considering the possibility of consumers' participation in demand response at different periods. The uncertain spot price is simulated by the stochastic scenario method, and a model for retailer's decision under multiple contracts is proposed by Conditional Value-at-risk assessment to maximize the expected profit. The presented model is a mixed-integer nonlinear programming problem, which is solved by CPLEX solver of GAMS software. The feasibility of the given decision strategy is verified through the numerical results.

Optimal scheduling of Park level integrated energy system considering electrothermal coupling

Considering the inherent characteristics of the park heating load, such as transmission delay, fuzzy heating comfort, etc., it can be used as a flexible load to participate in the optimal scheduling. Aiming at the minimum operation cost of the integrated energy system in the park, a collaborative optimal scheduling model of the park's integrated energy system with the participation of comprehensive demand response of electric heating load is constructed. The simulation results show that, compared with the optimization results of traditional power demand response, the application of integrated demand response of electric heating load improves the flexibility of production of cogeneration units in the park, reduces the total energy consumption cost of demand side users and the operation cost of the system on the premise of ensuring the balance of supply and demand of the system, improves the energy utilization efficiency, and realizes the environmental protection and economy of the system function.

Energy consumption modeling of production process for industrial factories in a day ahead scheduling with demand response

Abstract Industrial electricity demand is growing rapidly, whereby, energy consumption modeling and optimization techniques in industries has attracted significant attention in recent years. In this paper, a new model of energy consumption in the production process of aluminum, steel and cement is presented in accordance with a linear piece-wise approximation (LPWA) method. The proposed model is subsequently implemented in the day ahead energy management scheduling of a Microgrid (MG) (involving industrial factories). In order to increase efficiency and give industries an opportunity to contribute in the energy and ancillary services markets, demand response (DR) programs are implemented. The proposed scheduling model considers all the constraints of industrial factories and the MG to maximize their revenue. The performance of the proposed model is evaluated using three case studies. The first and second case studies respectively investigate the effectiveness of the proposed model with and without the implementation of DR programs. In the third case study, the coordination between industrial factories and a MG is investigated. Finally, the results show that the implementation of DR programs and participation of industrial factories in the energy and ancillary services markets, have improved the demand curve, hence increasing the revenue of the MG and industrial factories.

Peak shaving potential analysis of distributed load virtual power plants

As an important demand-side management method, distributed load participation in demand response provides an important means for auxiliary services such as peak-clipping and calming fluctuations of renewable power generation. Air conditioning load is usually viewed as a typical thermostatically related load, and is widely distributed in residential areas. Residential air conditioning load is a kind of demand response resource with broad application prospect. During the peak load period in summer, air conditioning load accounts for more than 40% of urban electricity consumption. Moreover, air conditioning load has certain flexibility and controllability. Reasonable evaluation of its peak clipping potential can effectively promote the safety and economic efficiency of the power grid. This paper proposes an analysis method of air conditioning load demand response potential combined with electricity consumption data and physical model. Subsequently, the switching machine and working state of air-conditioning load are identified, model parameters are calculated, and the dynamic adjustment potential of air-conditioning load in the future period is analyzed. In this paper, the data of electricity consumption in a province of China is used in the case study, and the algorithm is verified by examples.

ADMM‐based distributed response quantity estimation: a probabilistic perspective

With the wide deployment of smart meters in the end-user side, demand response (DR) is gaining prominence. Estimating the potential response is a preliminary step to the DR implementation. However, how to select proper features, how to protect privacy, and how to capture the response uncertainty remains three challenges to the customer response potential estimation. This study provides a detailed response quantity estimation for each customer. The authors proposed a probabilistic response quantity estimation framework and solved the problem by alternating direction method of multipliers (ADMM) in a fully-distributed way. In particular, by utilising a similar consumption pattern matching principle, the feature for each DR day of each customer is selected based on the matched DR participants. Then, the pinball loss-guided ridge regression is formulated, so the quantiles are obtained to cope with the uncertainty. The training process can be solved by each customer at the local site in a fully-distributed way, which protects the privacy and reduces the centralised processing burden. Finally, in the case study, the assumption behind a similar consumption pattern matching principle is validated empirically. Also, the proposed method is confirmed to have good convergence performance and can produce reasonable estimation results.

Demand-Side Participation via Economic Bidding of Responsive Loads and Local Energy Resources

The active participation of demand response (DR) resources into the wholesale market price formation and load dispatch process has the potential to stimulate demand-side flexibility. However, it is challenging for a market entity to utilize the DR resources for practical use. This is because day-ahead wholesale market-clearing prices are uncertain, and DR resources are heterogeneous. Furthermore, DR participation may lead to violations of the distribution system’s operational constraints. In this article, we propose an approach for an aggregator/load-serving entity (LSE) to profitably bid aggregated DR resources into the day-ahead wholesale market. The LSE requires an optimal bidding strategy that reflects the price elasticity of the aggregated retail loads to participate in the wholesale market operations. In the proposed approach, the LSE executes load curtailment and load shifting contracts with DR resources, where DR resources are remunerated for their participation at pre-contracted incentive prices. Then, the LSE aggregates the DR flexibility and optimally bids it in the day-ahead wholesale market. The proposed approach is validated using the IEEE-123-bus test system. It is demonstrated that the LSE can successfully generate economic bids for its participation in the day-ahead market by optimal management of DR resources and without violating the network’s operating constraints.

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique.

Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC.