Proposed Demand Response Model Research Articles

Demand Side Management in Smart Grid: A Dynamic-Price-Based Demand Response Model

Demand side management (DSM) is an important way to achieve smart energy management. Herein, a dynamic price (DP)-based demand response (DR) model is developed for DSM in smart grid. The proposed DR model can shift the peak electricity demand, thereby improving the power system stability and reliability. In a district-scale smart grid with a high photovoltaic power penetration, the energy service provider (ESP) optimizes the DP to maximize its utilities and reduce the load fluctuation while minimizing the bills and dissatisfaction of electricity consumers (ECs). The game theory model is used to explore the interaction between ESP and ECs, and the existence of Nash equilibrium is proved. The proposed DR model is validated with real-world data from a commercial and residential cluster in Suzhou City, Jiangsu Province, China. The results show that the peak electricity demands of commercial and residential ECs decreased by 4.99% and 9.99%, respectively, through the proposed DR model. Meanwhile, the ESP's net profits increased by 7.13% and 2.37%, respectively, while ensuring the ECs’ benefits. The results also demonstrate that the proposed DR model is robust in different scenarios. This article contributes to the effectiveness and efficiency of energy engineering management.

Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties

A community-integrated energy system under a multiple-uncertainty low-carbon economic dispatch model based on the deep reinforcement learning method is developed to promote electricity low carbonization and complementary utilization of community-integrated energy. A demand response model based on users’ willingness is proposed for the uncertainty of users’ demand response behavior; a training scenario set of a reinforcement learning agent is generated with a Latin hypercube sampling method for the uncertainties of power, load, temperature, and electric vehicle trips. Based on the proposed demand response model, low-carbon economic dispatch of the community-integrated energy system under multiple uncertainties is achieved by training the agent to interact with the environment in the training scenario set and reach convergence after 250 training rounds. The simulation results show that the reinforcement learning agent achieves low-carbon economic dispatch under 5%, 10%, and 15% renewable energy/load fluctuation scenarios, temperature fluctuation scenarios, and uncertain scenarios of the number of trips, time periods, and mileage of electric vehicles, with good generalization performance under uncertain scenarios.

A data-mining based optimal demand response program for smart home with energy storages and electric vehicles

In recent years, modern appliances with high electricity demand have played a significant role in residential energy consumption. Despite the positive impact of these appliances on the quality of life, they suffer from major drawbacks, such as serious environmental concerns and high electricity bills. This paper introduces a consolidated framework of load management to alleviate those drawbacks. Initially, benefiting from a demonstrative analysis of home energy consumption data, controllable and responsive appliances in smart home are identified. Then, the energy consumption pattern is reduced and shifted using flexible load models and better utilization of existing energy storage systems. This can be achieved through data mining approaches, i.e., density-based spatial clustering of application with noise (DBSCAN) method. In this technique, no sensor for detection or measurement instruments will be required, whose deployment incur cost to the system or increase security risk for consumers. In the following, one scheduling of using controllable appliances, which is formulated by convex optimization, is considered for the demand response (DR) program, provided that this plan doesn't affect customers’ priority and convenience. In the last stage, the deployment of energy storage systems, such as plug-in hybrid electric vehicles (PHEVs) and battery energy storage systems (BESS), is introduced to lower the energy cost and improve the performance of the proposed DR model. Simulation results of this demand response are compared with conventional k-clustering methods to confirm the economic superiority of the DBSCAN clustering technique using the data of a residential unit during three different scenarios.

A bi-level optimization framework for investment planning of distributed generation resources in coordination with demand response

ABSTRACT In this study, a multiyear distributed generation (DG) and battery energy storage system (BESS) investment planning with the coordination of demand response (DR) is presented for the distribution system. This coordinated system planning problem aims to maximize the net present value (NPV) profit related to the cost of energy purchased from the grid, energy losses, emission penalty cost, demand deviation penalty, operation and maintenance (OM) cost as well as investment cost of renewable power generation. To handle the high complexity of the investment planning problem, a bi-level optimization framework is used. The level-1 determines the optimal sizing of multiple BESS in presence of high penetration of PVs into the system. This level is used to minimize the net present cost (NPC) for energy consumption, greenhouse gas (GHG) emission, load deviation penalty, energy loss, investment and OM cost of PVs and BESS. In level-2, the optimal BESS power dispatch in coordination with the DR aggregator is obtained. The level-2 framework is used to minimize NPC for voltage deviation penalty and network losses with the scheduling of BESS and DR only. The proposed framework is simulated on the 33-bus distribution system to obtain the best investment plan for the distribution system operator (DSO). The results show that the NPV benefits of the proposed DR model with optimal integration of BESSs and PVs are 17.94 M$ and payback period of 9 years of the 20 years of planning horizons which is very significant compared to non-DR-based investment planning besides other technical benefits of DR implementation such as improvement in mean voltages, power loss, etc.

Modeling demand response based on utility function considering wind profit maximization in the day-ahead market

In this paper, behavior of electricity customers is modeled using utility function. The utility function which measures customer’s preferences is an important concept in microeconomics. This function interprets how a rational consumer would make consumption decisions. Based on the utility function, an economic demand model is developed as a function of customer risk aversion behavior to consider the effect of incentive payment on electricity consumption. The proposed model is designed to maximize the individual customer’s welfare under incentive-based demand response (DR) programs. Then, the DR is used to maximize wind power profit in the day-ahead market. To this end, the wind power producer can either use real time balancing market or DR to cope with the energy submitted in the day-ahead market and the delivered energy. To determine the day-ahead and balancing market prices, a probabilistic two-step market clearing optimization is solved considering uncertainties of wind power and behavior of electricity customers. The results show that as risk aversion behavior increases, more incentive payment is needed to convince the customer to reduce his/her demand. The numerical results demonstrate effectiveness of the proposed DR model in improving the wind generation profit in the day-ahead market.

Modelling of multi-timescale demand response for power markets

Demand response as an important part of smart grid makes the consumers participate in market operation that can lead to the benefit of both the utility and the consumers. In this paper, a multi-timescale demand response model that accounts for short-term and long-term demand characteristics is established. The short-term demand response and long-term demand response are combined by the electricity price elasticity matrix. The objective of optimisation is to minimise the peak-valley load difference in the total load, and particle swarm optimisation is adopted as the optimisation algorithm. The simulation results of IEEE-30 node distribution network show the feasibility and effectiveness of the proposed demand response model and pricing strategy. The research of this paper will provide technical support for the application of demand response mechanism after the new electric power reform.

An improved incentive-based demand response program in day-ahead and intra-day electricity markets

By advancement and vogue of smart grid technologies, there is a strong attitude toward utilizing different strategies for participating in demand response (DR) programs in electricity markets. DR programs can be classified into two main categories namely incentive-based programs (IBPs) and time-based rate programs (TBRPs). In this paper, an improved incentive-based DR (IBDR) model is proposed. In our proposed IBP, the concept of elasticity is improved where it depends not only on the electricity price, but also is a function of consumption hour and customer type. In this program, the incentive value which is paid to the participating consumers is not a fix value and relates to the peak intensity of each hour. The proposed IBP can participate in both of day-ahead and intra-day electricity markets. The property of considering intra-day market enables consumers to provide maximum DR if possible. The proposed model is implemented on peak load curve of Spanish electricity market and a 200-unit residential complex. Different scenarios are considered to show effectiveness of the proposed DR model from various aspects including peak shaving as well as economic indices.

A Novel Demand Response Model with an Application for a Virtual Power Plant

In modern power systems and electricity markets, demand response (DR) programs play an important role enabling the mitigation of critical load periods or price-peaking scenarios, thereby improving system reliability. Price fluctuations, in forward or real-time markets, can be an effective price-based DR mechanism for curtailing or shifting load. However, using dynamic pricing to achieve a desired load profile requires both an accurate demand forecast and knowledge of the price elasticity of demand, which is notoriously difficult to estimate. The limited accuracy of these parameter estimates is the main source of uncertainty limiting appropriate DR implementation. In this paper, we present a novel DR scheme that avoids the need to predict the price elasticity of demand or demand forecast, yet still delivers a significant DR. This is done based on the consumers' submissions of candidate load profiles ranked in the preference order. The load aggregator then performs the final selection of individual load profiles subject to the total system cost minimization. Additionally, the proposed DR model incorporates a fair billing mechanism that is enhanced with an ex post consumer performance tracking scheme implemented in a context of a virtual power plant aggregating load and generation units.

ISO's Optimal Strategies for Scheduling the Hourly Demand Response in Day-Ahead Markets

This paper presents a hierarchical demand response (DR) bidding framework in the day-ahead energy markets which integrates customer DR preferences and characteristics in the ISO's market clearing process. In the proposed framework, load aggregators submit aggregated DR offers to the ISO which would centrally optimize final decisions on aggregators' DR contributions in wholesale markets. The hourly load reduction strategies include load shifting and curtailment and the use of onsite generation and energy storage systems. The ISO applies mixed-integer linear programming (MILP) to the solution of the proposed DR model in the day-ahead market clearing problem. The proposed model is implemented using a 6-bus system and the IEEE-RTS, and several studies are conducted to demonstrate the merits of the proposed DR model.

Reliability assessment of incentive- and priced-based demand response programs in restructured power systems

Fostering demand response (DR) through incentive-based and priced-based programs has always great impact on improvement of efficiency and reliability of the power systems. The use of DR lowers undesirable effects of failures that usually impose financial costs and inconveniences to the customers. Hence, quantifying the impact of demand response programs (DRPs) on reliability improvement of the restructured power systems is an important challenge for the independent system operators and the regional transmission organizations.In this paper, the DR model which treats consistently the main characteristics of the demand curve is developed for modeling. In proposed model, some penalties for customers in case of no responding to load reduction and incentives for customers who respond to reducing their loads are considered.In order to make analytical evaluation of the reliability, a mixed integer DC optimal power flow is proposed by which load curtailments and generation re-dispatches for each contingency state are determined. Both transmission and generation failures are considered in contingency enumeration. The proposed technique is modeled in the GAMS software and solved using CPLEX as a powerful mixed integer linear programming (MILP) solver. Both supply-side reliability for generation companies and demand-side reliability for customers are calculated using this technique.In order to simulate customers’ behavior to different DRPs in a real power network, the proposed DR model is used and evaluated over Iranian power network. In order to investigate the reliability effects of DRPs based on proposed reliability method, DRPs based on the DR model are implemented over the IEEE RTS 24-bus test system, and reliability indices for generation companies, transmission network and customers are calculated. Using proposed performance index, the priority of the DRPs are determined from view point of customers, generation companies, transmission network and the whole system in IEEE RTS.