Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

Optimal Demand Response of Controllable Loads in Isolated Microgrids

The electric power industry worldwide has undergone significant changes over the last decade. Environmental compliance and energy conservation issues have occupied the forefront of the new age power system which have opened the possibility of an increased integration of Distribution Energy Resources (DER). With the presence of DERs, reliable system operation and control has become increasingly difficult as the power flow no longer remains unidirectional. Microgrids with their decentralized system operations offer solutions to the challenges posed by this transformation. It has been generally regarded that the key to increased operational efficiency and economy of microgrids especially under its isolated mode of operation lies with improved customer participation in Demand Response (DR) programs. Developing a DR scheme with a novel customer interaction inside a microgrid setup will provide a key solution that would drive the system performance to its peak. This thesis proposes a mathematical model of DR integrated into the generation scheduling problem of an isolated microgrid. Controllable demand is modeled as a function of external parameters such as outside temperature, Time-of-Use (TOU) pricing and maximum limit on demand Pmax through supervised learning of neural networks. An optimal DR model is proposed to learn the load behavior and produce a control action on the controllable load profile of the end users. A novel Microgrid Energy Management System (MEMS) is proposed as the central unit of this DR model to determine the control signal and perform a least-cost operational schedule of the microgrid. Realistic data from an actual Energy Hub Management System (EHMS) is used to better replicate the real-world modeling scenario. Continuing with the DR model, the effect of customer response through energy payback model is also studied. The impact of this response on the customer load profile and an estimate of the expected peak reduction is also presented. The proposed model and the case studies are simulated on an CIGRE IEEE Medium Voltage (MV) benchmark system. The system under consideration is an appropriate approximation of the actual isolated microgrid system with their dispatchable diesel generators, Energy Storage System (ESS), photovoltaic (PV) panels and wind turbines. Finally, the results illustrating the effectiveness of the proposed DR scheme and the computational procedures are discussed. This work is concluded by exploring the possible research directions while addressing some pertinent problems for the same

Sharing Economy in Local Energy Markets

With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of 'access over ownership', the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.</p

Bidding Strategy for Networked Microgrids in the Day-Ahead Electricity Market

In recent years, microgrids have drawn increasing attention from both academic and industrial sectors due to their enormous potential benefits to the power systems. Microgrids are essentially highly-customized small-scale power systems. Microgrids’ islanding capability enables microgrids to conduct more flexible and energy-efficient operations. Microgrids have proved to be able to provide reliable and environmental-friendly electricity to quality-sensitive or off-grid consumers. In addition, during the grid-connected operation mode, microgrids can also provide support to the utility grid. World-widely continuous microgrid deployments indicate a paradigm shift from traditional centralized large-scale systems toward more distributed and customized small-scale systems. However, microgrids can cause as many problems as it solves. More efforts are needed to address these problems caused by microgrids integration. Considering there will be multiple microgrids in future power systems, the coordination problems between individual microgrids remain to be solved. Aiming at facilitating the promotion of microgrids, this thesis investigates the system-level modeling methods for coordination between multiple microgrids in the context of participating in the market. Firstly, this thesis reviews the background and recent development of microgrid coordination models. Problems of existing studies are identified. Motivated by these problems, the research objectives and structure of this thesis are presented. Secondly, this thesis examines and compares the most common frameworks for optimization under uncertainty. An improved unit commitment model considering uncertain sub-hour wind power ramp behaviors is presented to illustrate the reformulation and solution method of optimization models with uncertainty. Next, the price-maker bidding strategy for collaborative networked microgrids is presented. Multiple microgrids are coordinated as a single dispatchable entity and participate in the market as a price-maker. The market-clearing process is modeled using system residual supply/demand price-quota curves. Multiple uncertainty sources in the bidding model are mitigated with a hybrid stochastic-robust optimization framework. What’s more, this thesis further considers the privacy concerns of individual microgrids in the coordination process. Therefore a privacy-preserving solution method based on Dantzig-Wolfe decomposition is proposed to solve the bidding problem. Both computational and economic performances of the proposed model are compared with the performances of conventional centralized coordination framework. Lastly, this thesis provides suggestions on future research directions of coordination problems among multiple microgrids