4 - Demand response: An enabling technology to achieve energy efficiency in a smart grid

Demand Response (DR) is an important ingredient of the emerging Smart Grid paradigm. Although much of the DR emerging under Smart Grid is targeted at the distribution level, DR programs are regarded as important elements for reliable and economic operation of the transmission system and the wholesale markets. In the context of Energy and Ancillary Service markets facilitated by the ISOs/RTOs, DR programs may be broadly classified as market-based or reliability-based depending on the conditions under which DR is deployed: The former provide for DR vis-a-vis economic signals, while the latter are generally triggered under emergency conditions. Depending on the ISO/RTO market design and applicable operational standards, the market products DR can offer may include Energy, Reserve Ancillary Services (i.e., Supplemental/Non-spinning Reserve, Spinning/Responsive Reserve, or Regulating Reserve), and Capacity. In this paper we first explore different facets of Demand Response under the Smart Grid paradigm. We then briefly summarize the existing and evolving Demand Response programs at different ISOs/RTOs and the product markets they can participate in. We conclude by addressing some of the challenges and potential solutions for implementation of DR under Smart Grid and Market paradigms.

In smart grid, customers have access to the electricity consumption and the price data via smart meters; thus, they are able to participate in the demand response (DR) programs. In this paper, we address the interaction among multiple utility companies and multiple customers in smart grid by modeling the DR problem as two noncooperative games: the supplier and customer side games. In the first game, supply function bidding mechanism is employed to model the utility companies’ profit maximization problem. In the proposed mechanism, the utility companies submit their bids to the data center, where the electricity price is computed and is sent to the customers. In the second game, the price anticipating customers determine optimal shiftable load profile to maximize their daily payoff. The existence and uniqueness of the Nash equilibrium in the mentioned games are studied and a computationally tractable distributed algorithm is designed to determine the equilibrium. Simulation results demonstrate the superior performance of the proposed DR method in increasing the utility companies’ profit and customers’ payoff, as well as in reducing the peak-to-average ratio in the aggregate load demand. Finally, the algorithm performance is compared with a DR method in the literature to demonstrate the similarities and differences.

The number of microgrids within a smart distribution grid can be raised in the future. Microgrid-based distribution network reconfiguration is analyzed in this research by taking demand response programs and power-sharing into account to optimize costs and reduce power losses. The suggested method determined the ideal distribution network configuration to fulfil the best scheduling goals. The ideal way of interconnecting switches between microgrids and the main grid was also identified. For each hour of operation, the ideal topology of microgrid-based distribution networks was determined using optimal power flow. The results were produced with and without the use of a demand response program and power-sharing in each microgrid. Different load profiles, such as residential, industrial, commercial, and academic, were taken into account and modified using appropriate demand response programs and power-sharing using the Artificial Bee Colony algorithm. Various scenarios were explored independently to suit the diverse aims considered by the distribution network operator for improved observation. The ABC optimization in this research attempted to reduce the system’s total operation costs and power losses through efficient networked microgrid reconfiguration. The results of optimal microgrid topology revealed the effects of power-sharing and demand response (TOU) programs. The results obtained in the proposed idea shows that costs were reduced by 8.3% and power losses were reduced by 4%. The IEEE 33-bus test system was used to demonstrate the effectiveness of the proposed approach.

Demand response (DR) programs and local markets (LM) are two suitable technologies to mitigate the high penetration of distributed energy resources (DER) that is vastly increasing even during the current pandemic in the world. It is intended to improve operation by incorporating such mechanisms in the energy resource management problem while mitigating the present issues with Smart Grid (SG) technologies and optimization techniques. This paper presents an efficient intraday energy resource management starting from the day-ahead time horizon, which considers load uncertainty and implements both DR programs and LM trading to reduce the operating costs of three load aggregator in an SG. A random perturbation was used to generate the intraday scenarios from the day-ahead time horizon. A recent evolutionary algorithm HyDE-DF, is used to achieve optimization. Results show that the aggregators can manage consumption and generation resources, including DR and power balance compensation, through an implemented LM.

This paper addresses implementation of Demand Response (DR) programs in competitive electricity markets. An overview of present status of the application of DR programs in major electricity markets is provided. In this paper, An objective-wised classification of DR measures is proposed which is rooted in practical DR experiences. Market opportunities and associated barriers are investigated. Further, enabling technologies for implementing DR programs are discussed. Finally, the role of smart grid in enabling DR is highlighted.

The necessity to seamlessly integrate emerging smart grid (SG) technologies to the electric distribution system has encouraged the development of demand-side management (DSM) and Demand Response (DR) programs. In particular, DR aggregators will play an important role in the new operational paradigm of SG. To propose a new three-phase optimal dispatch model for multiple DR aggregators. The model can consider several DR service providers by means of the explicit inclusion of their characteristics, in the form of cost functions and capacity, in an optimal dispatch model for DR support at the distribution system level. Hence, this proposal helps to eliminate any possible uncertainty about the provision of DR services by improving the traditional hierarchical scheme adopted based on prices issued by the independent system operator. By means of the unbalanced distribution system modeling, DR involvement of single- and three-phase consumers is considered avoiding aggravating the asymmetric balancing between phases, as opposed to traditional positive-sequence DR approaches. The model is implemented and solved combining the two state-of-the-art computational tools, Matlab® and OpenDSS. The former is used to solve the optimization problem, whereas the latter is used to perform numerical simulations of three-phase unbalanced power flow; both tools allow a straightforward model implementation resulting in a tool easily modified and updated. The effectiveness of the proposed approach is numerically demonstrated using the IEEE 13- and 123-node test feeders, in which undesirable operating scenarios are corrected by the implementation of the optimal dispatch of DR resources in very short computational times. Based on the results, it is shown how the ISO is capable to request DR considering several aggregators competing at the distribution system level. Finally, the loss reduction has been included in the objective function, showing that the proposal optimally dispatches the DR aggregators to conveniently minimize the ISO’s operational costs.

In a re-structured environment, demand response (DR) programs are presented in a new style. The demand response program consists of demand-side management methods which answers to electricity price variations. With advent of electricity markets, the demand-side management programs was introduced into two categories: (1) energy efficiency program, (2) demand response program. This paper studies engagement of participants load in an electricity market having a grid with large and small loads interested to participate in the market, energy storage systems, micro-grids, and distributed generations. In the proposed scheme, it is supposed to have an aggregator which sends demand-side preferences including load curtailment, load shifting, onsite generation, and energy storage systems along with proposed value and prices and all respective system constraints to the independent system operator (ISO). In fact, load aggregators submit aggregated DR offers to the ISO in order to optimize final decisions on aggregators’ DR contributions in wholesale market. The main goal of this paper is to solve the operation problem considering demand-side, system, and pollution and reliability constraints. Compared to other methods, the results indicate that the explicit modeling of customer DR would provide ISOs with more flexible options for scheduling the available energy resources in day-ahead energy markets.

Designing an optimal Demand Response (DR) program necessitates having information on the utility of users. However, that is not easily accessible since the users may not be willing to report their personal information. In this paper, we propose an incentive-based DR mechanism for Commercial and Industrial (C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\&amp;$</tex-math> </inline-formula> I) customers, leveraging contract theory to address the challenges of incomplete information and ensure the participation of customers in the DR program. Designing a DR mechanism includes specifying an incentive reward, penalty, and the corresponding demand reduction as functions of the customer’s utility information. These functions are obtained through an optimization problem that maximizes the grid operator’s profit, ensures truthful reporting of personal information and participation of the customers in the DR program. After formulating the optimal mechanism, the main technical challenge is reformulating the obtained non-convex and computationally inefficient optimization problem to a tractable and convex one. The extensive numerical evaluations demonstrate the effectiveness of our proposed framework in comparison to existing benchmark schemes. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Imbalances between supply and demand, particularly during the hot summer months, can result in issues such as power outages. This problem is detrimental for both grid operators and customers alike. DR programs have been implemented in numerous countries to address these challenges. C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\&amp;$</tex-math> </inline-formula> I customers, due to the schedulable nature of their loads, make for ideal targets for these programs. However, the main obstacle to implementing such programs is customer participation. Incentive rewards offered in these programs for load reduction are fixed for all customers, discouraging those with low rewards from participating and imposing high costs on grid operators for those with high rewards. On the other side, designing unique rewards and penalties for each customer necessitates information about their preferences, which grid operators cannot access. In this paper, we propose an incentive mechanism based on the contract between the grid operator and C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\&amp;$</tex-math> </inline-formula> I customers that ensures customers truthfully report their preferences while simultaneously maximizing grid operator profit and guaranteeing the participation of all customers. The proposed DR program can be implemented for large non-residential customers in order to reduce the peak load of the grid.

Demand response (DR) programmes offer to customers the opportunity to reduce the power peak and the energy consumption in response to a price signal or financial incentive. Typically, the request to reduce peak demands is made for a specific time period on a specific day, which is referred to as a DR event. To predict a reference energy consumption level in case of different buildings or blocks of buildings within the Technical University of Cluj‐Napoca, this study proposes an artificial intelligence enhanced energy profiling method and a more intuitive yet simple method for baseline determination, easy to understand, which allows all the interested parties to estimate the energy and economy savings after a DR event. Once the baseline electric load profile is established, the aim of this study is to calculate some predefined key performance indicators. The two baseline detection methods are compared with each other as a measure of DR event effectiveness. The study has been conducted to clearly demonstrate the economic and environmental benefits of controlling the aggregated load curve in blocks of buildings within several effectively applied DR programmes.

An important support in Demand Response (DR) programs can be provided by the Electric Vehicles (EVs). EVs may behave as a load to the grid, a supplier of electricity to the grid or an energy storage device. Thanks to the smart grid enabling technologies, Utilities can manage EVs charging time and rates, gather EVs-detailed meter data and, therefore, implement DR programs. In the present paper an overview on DR definitions and a classification of the types of customers involved and implementable programs is reported. Then a special focus on EVs integration in DR programs is given, stressing the valuable services that EVs in smart grid asset can provide.

Industrial power load scheduling considering demand response

A smart grid will integrate extensive distributed resources such as local generation sources, Plug-in Hybrid Electric Vehicles (PHEVs) and demand response (DR) programs. By using smart metering, advanced metering infrastructure (AMI) facilitates consumer participation in DR programs and changes the consumption pattern of residential user via tariff schemes for electrical energy. In addition, the smart grid will play an important role in controlling and coordinating of PHEVs charging or discharging activities. In this paper, an optimization technique is used, in order to minimizing a retailer energy cost. The retailer controls charging or discharging of PHEVs during a day and considers the impact of time variant tariff schemes on customer behavior. The behavior of customer is modeled by means of economic demand models.

Distributed energy resources have been increasingly integrated into electrical grids. Consequently, electricity markets are expected to undergo changes and become more complex. However, while there are many scientific publications on the topic, a broader discussion is still necessary. Therefore, a systematic literature review on electricity markets in the context of distributed energy resources integration was conducted in this paper to present in-depth discussions on the topic, along with shedding light on current perspectives, the most relevant sources, authors, papers, countries, metrics, and indexes. The software R and its open-source tool Bibliometrix were used to perform the systematic literature review based on the widely recognized databases Web of Science and Scopus, which led to a total of 1685 articles after removing duplicates. The results demonstrate that demand response, renewable energy, uncertainty, optimization, and smart grid are the most-used keywords. By assessing highly impactful articles on the theme, emphasis on energy storage systems becomes clear compared to distributed generation and electric vehicles. However, electric vehicles draw attention in terms of citations. Furthermore, multi-level stochastic programming is the most-applied methodology among highly impactful articles. Due to the relevance of the demand response keyword, this paper also conducts a specific review on the topic aligned with electricity markets and distributed energy resources (296 articles). The results demonstrate that virtually all high-impact publications on the topic address day-ahead or real-time pricing. Based on the literature found, this paper presents a discussion on the main challenges and future perspectives related to the field. The complexity of electrical power systems and electricity markets is increasing substantially according to what this study found. Distributed generation development is already advanced, while energy storage systems and electric vehicles are limited in many countries. Peer-to-peer electricity trading and virtual power plant are newer concepts that are currently incipient, and DR programs showcase an intermediate stage of evolution. A particular lack of research on social issues is verified, and also a lack of all-encompassing studies that address multiple interconnected topics, which should be better addressed in the future. The in-depth assessment carried out in this paper is expected to be of high value to researchers and policy-makers and facilitate future research on the topic.

This paper models a novel demand response (DR) trading strategy. In this model, the DR aggregator obtains the DR from the end-users via two types of DR programs, i.e. a time-of-use (TOU) program and an incentive-based DR program. Then, it offers this DR to the wholesale market. Three consumer sectors, namely residential, commercial and industrial, are included in this problem. The DR program is dependent on their corresponding load profiles during the studied time horizon. This paper uses a mixed-integer linear programming (MILP) problem and it is solved using the CPLEX solver through a stochastic programming approach in GAMS. The risk measure chosen to represent the load uncertainty of the users who are participating in the DR program is Conditional Value-at-Risk (CVaR). The proposed problem is simulated and assessed through a case study of a test system. The results indicate that the industrial loads play a major role in the power system and this directly affects the DR program. Moreover, the risk-averse decision-maker in this model favors a reduced participation in the DR programs when compared to a decision-maker who is risk-neutral, since the risk-averse decision maker prefers to be more secure against uncertainties. In other words, an increase in risk factor results in a decrease in the participation rate of the consumers in DR programs.

The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost