Demand Response Participation Research Articles

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.

Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, we designed a demand response participation strategy based on two-stage decisions to reduce the total cost of the datacenter while considering the DR requirements of the grid. The first stage determines whether to participate in demand response by predicting real-time electricity prices of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by reverse auction. Then, the datacenter selects the tasks of the winning users to postpone processing them with awards. Experimental results show that the proposed strategy could help the datacenter to reduce its cost and effectively meet the demand response requirements of the smart grid at the same time.

Many-criteria optimality of coordinated demand response with heterogeneous households

Abstract This paper proposes a bilevel multi-house energy management (MHEM) framework to coordinate the residential demand response (DR) of heterogeneous households based on many-criteria optimality. In the upper level, the loss of life (LOL) cost of transformers is formulated into the DR cost model, and a stochastic scheduling is implemented to determine the optimum amount of transformer load deferment and curtailment. The lower level aims to optimally allocate the transformer load from the aggregator to individual households, and a many-criteria DR optimality model is proposed to maximize the multi-house benefits from DR while achieving coordination of the DR participation. Furthermore, a hypercube spatial transformation based classification and sorting scheme is developed to form an evolutionary many-objective (EMO) algorithm in order to solve the many-criteria decision making (MCDM) problem of coordinated DR with numerous participants. The performance of the proposed method was benchmarked and validated on different scaled neighborhood systems over a 24-h scheduling horizon, and comparative results demonstrated its superiority and optimality in solving many-house DR problems.

A Two-stage Demand Response Strategy for Datacenters in the Smart Grid Environment

As a large load of the smart grid, datacenters usually have a huge demand for power. Datacenter participation in demand response programs of smart grids can help the grid to adjust the load while reducing its own power costs so as to save electricity costs. In this paper, we designed a two-stage demand response participation strategy to alleviate the peak load pressure of the gird and reduce the power cost of the datacenter. The first stage determines whether to participate in demand response according to the real-time electricity prices variation of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by auction. Then the datacenter selects the tasks of the winning users to postpone processing with awards, which reduces the electricity costs of the datacenter and effectively meets the demand response requirements of the smart grid. In this way, the goals of both parties could be achieved.

Mechanism Design for Participation of Demand Response in China Power Market

At present, with the rapid development of demand response in China, various market players such as electric vehicles, energy storage, virtual power plants, and load aggregators are constantly enriched. However, due to the imperfect market-oriented trading mechanism, the potential and enthusiasm of demand response have not been fully released. With the accelerated construction of China’s power market, various trading mechanisms such as spot market and ancillary services market have gradually improved. Based on the development of China’s power market, this paper designs a market-based transaction mechanism for demand response, and proposes a future development path for demand response. In the end, the subsidy for demand response development in China is estimated, which supports the market-oriented participation path of demand response in China’s power market.

Risk-averse probabilistic framework for scheduling of virtual power plants considering demand response and uncertainties

In this paper, a risk-based stochastic framework is presented for short-term energy and reserve scheduling of a virtual power plant (VPP) considering demand response (DR) participation. The VPP comprises several dispatchable generation units, battery energy storage systems (BESSs), wind power units, and flexible loads. The proposed scheduling framework is formulated as a risk-constrained stochastic program to maximize the VPP’s profit considering uncertainties of loads, wind energy and electricity prices as well as N-1 contingencies. The proposed model considers both supply and demand-sides capability for providing and deploying reserves in order to optimize the use of resources while satisfying N-1 security and other constraints. Moreover, the effect of risk-aversion on decision making of the VPP in the offering/bidding power and required reserve services is investigated by implementing conditional value-at-risk (CVaR) in the optimization model. The proposed scheme is implemented on a test VPP and the energy and reserve scheduling with and without DR participants is addressed in detail through a numerical study. Moreover, the effects of the operator’s risk-averse behavior on the VPP energy and reserve management and its security indices are investigated.

Human Comfort-Based-Home Energy Management for Demand Response Participation

The residential building sector is encouraged to participate in demand response (DR) programs owing to its flexible and effective energy resources during peak hours with the help of a home energy management system (HEMS). Although the HEMS contributes to reducing energy consumption of the building and the participation of occupants in energy saving programs, unwanted interruptions and strict guidance from the system cause inconvenience to the occupants further leading to their limited participation in the DR programs. This paper presents a human comfort-based control approach for home energy management to promote the DR participation of households. Heating and lighting systems were chosen to be controlled by human comfort factors such as thermal comfort and visual comfort. Case studies were conducted to validate the proposed approach. The results showed that the proposed approach could effectively reduce the energy consumption during the DR period and improve the occupants’ comfort.

Assessing the demand response capacity of U.S. drinking water treatment plants

This study assesses the spatio-temporal electricity consumption and demand response (DR) capacity of U.S. drinking water treatment plants (DWTPs). We account for the installed unit processes at each plant, the electricity intensity of each unit process, the compatibility of a unit process with DR participation, and the approximate volume of water treated at each DWTP during different months of the year. We then perform a parametric analysis to calculate the shiftable load from DWTPs as a function of the length of the load curtailment period (TC), the ratio of the maximum treatment capacity to the peak-day demands of DWTPs (rmax), and the time of the year. The results of the parametric analysis suggest that the total DR capacity of U.S. DWTPs in 2018 varied between 140 MW and 610 MW as a function of TC, rmax and time of the year. We also find that the total electricity use by DWTPs has increased 25% from 2013 to 2018, largely due to the rapidly increasing adoption rate of reverse osmosis processes. These results indicate that DWTPs provide only minimal DR capacity in most locations in the U.S, but that further electrification of the drinking water treatment sector may significantly increase this DR capacity in critical geographic locations during peak summer months.

A Machine Learning Pipeline for Demand Response Capacity Scheduling

Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.

Risk-Constrained Bidding Strategy for Demand Response, Green Energy Resources, and Plug-In Electric Vehicle in a Flexible Smart Grid

The flexibility of smart grids has become an important issue due to the increasing penetration of uncertain energy resources, such as renewable as well as virtual power plants in the smart grids. Flexibility sources, such as demand response (DR) programs and plug-in electric vehicles (PEVs), can help the smart grid to be more productive. Although the renewable power plants are considered as flexible tools, they are somehow uncertain by themselves. In this article, the uncertainty of power generation of renewable resources has been resolved by incorporating the DR programs and PEVs. A stochastic decision making model for the coordinated operation of renewable resources and some virtual power generation is presented to solve a risk-constrained optimal bidding strategy for a smart grid. The participation of DR and PEV aggregators in the day-ahead market is considered. The uncertainty in day-ahead prices associated with renewable power generation is discussed throughout this article. As a well-known measure, the conditional value at risk is employed in the model to cope with all aforementioned uncertainties. Numerical studies and result analysis show that the expected profit of these resources is increased and the related risk is reduced significantly.

Demand Response Cooperative and Demand Charge

In this paper, we show that a group of Demand Response (DR) providing customers will benefit by transferring into a cooperative. While the benefit of aggregation is definite when unconstrained, this paper is devoted to studying benefits of a DR cooperative with a nonlinear piecewise objective function of minimizing net cost comprising energy charges and demand charges while being constrained by power system constraints and other physical constraints. We propose a two-level stochastic optimization formulation that provides a model for DR cooperatives to aggregate and schedule participating resources with the objective of minimizing the total electricity bill comprising energy and demand charges. It also provides a settlement tool for the cooperative to share costs and benefits among its members. In the first level, a stochastic mixed integer linear optimization formulation to minimize the total resource cost for the electricity market is solved considering various probabilistic scenarios to find locational marginal prices (LMPs) for 24-hours and the corresponding amount of DR scheduled for DR participants including the cooperative. In the second level, a linear optimization formulation is solved to schedule DR of cooperative members with the objective of minimizing the total electricity bill of the cooperative comprising energy and demand charges. Finally, we present a settlement tool to share the costs and benefits amongst the cooperative members, whereby all members benefit from reduced electricity bills. The proposed model was tested on modified IEEE 6-bus and 118-bus systems. The results conclusively demonstrate the benefit of the proposed cooperative model for DR where scheduling DR in concert by members has a pronounced effect in reducing demand charges for all more than that achieved by individual members minimizing their own electricity bills.

Non-Cooperative Indirect Energy Trading with Energy Storage Systems for Mitigation of Demand Response Participation Uncertainty

This work focuses on the demand response (DR) participation using the energy storage system (ESS). A probabilistic Gaussian mixture model based on market operating results Monte, Carlo Simulation (MCS), is required to respond to an urgent DR signal. However, there is considerable uncertainty in DR forecasting, which occasionally fails to predict DR events. Because this failure is attributable to the intermittency of the DR signals, a non-cooperative game model that is useful for decision-making on DR participation is proposed. The game is conducted with each player holding a surplus of energy but incomplete information. Consequently, each player can share unused electricity during DR events, engaging in indirect energy trading (IET) under a non-cooperative game framework. The results of the game, the Nash equilibrium (N.E.), are verified using a case study with relevant analytical data from the campus of Gwangju Institute of Science and Technology (GIST) in Korea. The results of the case study show that IET is useful in mitigating the uncertainty of the DR program.

Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction

Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings. This paper proposes a pricing strategy to help EUCs and building operators achieve an optimal DR of price-elastic HVAC systems, considering peak load reduction. The proposed strategy is implemented by adopting a bi-level decision model. The nonlinear thermal response of an experimental building room is modeled using piecewise linear equations, which helps convert the bi-level model to the single-level model. The pricing strategy is implemented considering a time-of-use (TOU) pricing scheme, leading to low price volatility. Case studies are conducted for two types of load curves and the results demonstrate that the proposed strategy helps EUC promote the price-based DR of the commercial buildings for conventional load curves. However, EUC cannot reduce the peak load on duck curve caused by the large introduction of photovoltaic generators, even with price-sensitive HVAC systems in commercial building. This will be addressed in future studies by inducing DR participation of HVAC systems in residential buildings.

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

The flexibility deployment of the service sector - A demand response modelling approach coupled with evidence from a market research survey

The flexible use of energy is seen as a key option to facilitate the integration of volatile renewable energy sources (RES) into the electricity sector. In this study, we focus on flexibility in the service sector, in terms of flexible technologies, experiences and willingness to participate in demand response (DR) actions. We analyse the technically possible future deployment of flexibility, the practically possible deployment of flexibility and also take the reduction of RES surplus electricity into account. Our results are based on survey data from over 1.500 service sector companies (offices, trade, hospitality) and modelling results with a time resolved DR model (eLOAD). The data show that service sector companies have few experiences in DR so far, which is among others caused by the unfavourable regulatory conditions to participate in flexibility markets. The currently most common forms of DR are load shedding and flexible tariffs and optimized purchase of electricity. Participation in DR varies between subsectors and company sizes, but on average all subsectors are interested in extending (automated) DR measures in the future. Our projections result in a possible technical deployment of flexible electricity of 7.74 TWh of which about 510 GWh can be used to reduce renewable surplus electricity (in case of a 50% RES share). In case of a 80% RES share, this can reach 1.63 TWh. Integrating the willingness of companies to participate in DR, the practical possible deployment results in 131 GWh reduction of renewable surplus electricity. This can be interpreted as a first-mover potential for DR. Future increased need for flexible demand could raise the profit for the companies and their willingness in participating in DR. Further analyses on most promising target groups of companies would help to tap the potentials and to create market offers as well as policies to incentivise participation.

Demand Response Strategy of Energy Prosumer Based on Robust Optimization Through Aggregator

In this article, the optimal operation strategy for the aggregator to participate in demand response (DR) market is proposed. First, on the day before the occurrence of DR, Customer Baseline Load (CBL)-based load forecasting is performed using historical load data and a day-ahead scheduling is implemented to minimize electric charges by using peak reduction and arbitrage trading. If the demand response occurs, distributed energy resources (DERs) bid power reduction capacity to the aggregator. In Korea tariff system, demand charges determined by peak load are very expensive. Therefore, DERs should not update their peak load due to demand response market participation. The uncertainty of load prediction is modeled using the average value of mean absolute percentage error (MAPE), and robust optimization (RO) is implemented to determine a bidding capacity, thereby preventing the peak form being updated due to prediction error. Then, the aggregator decides the capacity to participate in DR market by considering the bidding capacity and priority. It presents the method to determine the incentive for participation in DR using a logarithmic barrier function. To evaluate the performance of the proposed algorithm simulation was performed by constructing a scenario for prediction error and mandatory reduction capacity.

Optimal scheduling of building integrated energy system based on demand response

With the development of renewable energy technology, integrating a variety of renewable energy integrated energy systems can effectively solve the problem of optimizing the scheduling of buildings with high energy consumption and fast growth rate. Based on the modeling and analysis of various energy equipment in the system, the integrated energy system of building buildings, based on the demand response compensation price, with the lowest construction operating cost as the goal function, establishes the optimization scheduling model of building-level integrated energy system based on demand response, and uses the particle group algorithm based on cloud model improvement to optimize the solution of the model. The study is introduced for simulation to compare the two different modes of participation in demand response, and the optimal performance of cloud model particle group algorithm and elementary particle group algorithm. The results show that the cloud model particle group algorithm model based on demand response can effectively save the operating cost of the building-level integrated energy system, and reduce the power grid side load peak and valley difference.

Diagnosis of Demand Response Participants: Detection of Defaulting Participants by Sparse Reconstruction

Diagnosis of Demand Response Participants: Detection of Defaulting Participants by Sparse Reconstruction

Aggregated Demand Response Scheduling in Competitive Market Considering Load behavior through Fuzzy Intelligence

This article presents an integrated and intelligent price based self-scheduling approach for demand response (DR) aggregators in the wholesale market considering load profile attributes of aggregated loads. The load profile attributes namely utilization and availability factors are derived as a function of load type and temporal characteristics of responsive loads. The proposed integrated approach uses the concept of conventional random willingness factors coupled with load profile attributes to resolve the ambiguity around customer/load behavior. The response of the customer to load profile attributes and the willingness factor is formulated using three different models, namely, linear, exponential, and nonlinear models. Further, to consider the nondeterministic and indistinct behavior of the customer, a fuzzy inference system (FIS) is developed to create a relationship between the load profile attributes and willingness to DR participation cost. Further, an online FIS membership function parameter tuning mechanism is developed to improve the performance of DR aggregator as well as overall day ahead market. The proposed approach is simulated for a combined price based scheduling of the generation company and the DR aggregator with responsive loads spread over various load sectors such as industrial, commercial, residential, agricultural, and the municipal sector. The simulation results of the online intelligent-integrated framework are compared to conventional willingness model, nonfuzzy, and untuned FIS models with and without considering load profile and customer willingness factor. The comparison of the same demonstrates the effectiveness of the proposed online framework in improving the overall and DR aggregator surplus over other approaches.