A Compendium of Performance Metrics, Pricing Schemes, Optimization Objectives, and Solution Methodologies of Demand Side Management for the Smart Grid

Abstract

The curtailing of consumers’ peak hours demands and filling the gap caused by the mismatch between generation and utilization in power systems is a challenging task and also a very hot topic in the current research era. Researchers of the conventional power grid in the traditional power setup are confronting difficulties to figure out the above problem. Smart grid technology can handle these issues efficiently. In the smart grid, consumer demand can be efficiently managed and handled by employing demand-side management (DSM) algorithms. In general, DSM is an important element of smart grid technology. It can shape the consumers’ electricity demand curve according to the given load curve provided by the utilities/supplier. In this survey, we focused on DSM and potential applications of DSM in the smart grid. The review in this paper focuses on the research done over the last decade, to discuss the key concepts of DSM schemes employed for consumers’ demand management. We review DSM schemes under various categories, i.e., direct load reduction, load scheduling, DSM based on various pricing schemes, DSM based on optimization types, DSM based on various solution approaches, and home energy management based DSM. A comprehensive review of DSM performance metrics, optimization objectives, and solution methodologies is’ also provided in this survey. The role of distributed renewable energy resources (DERs) in achieving the optimization objectives and performance metrics is also revealed. The unpredictable nature of DERs and their impact on DSM are also exposed. The motivation of this paper is to contribute by providing a better understanding of DSM and the usage of DERs that can satisfy consumers’ electricity demand with efficient scheduling to achieve the performance metrics and optimization objectives.

A Survey on Home Energy Management

“…The present survey aims to provide a comprehensive update on HEMSs literature, focusing on: (i) appliance scheduling approaches [8], [26], [35], [91], [92]; (ii) constrained scheduling problem in terms of goals and constraints [8], [26], [35] and SOP and MOP formulations [8]; (iii) categorisation models of appliances, which are typically considered on HEMSs [5], [8], [26], [35]; (iv) cyber-security in terms of vulnerabilities, attack vectors and countermeasures [230], [233]. A recent survey worth to be mentioned for its thorough and clear review on energy management solutions, performance metrics and optimisation objectives to improve consumption-side energy usage is that of [253], but it lacks in addressing issues concerning to distributed energy resources in new generation smart power grids. These issues, in terms of smart grid techniques and their contributions to rational energy usage are covered in the present survey, being their shortcomings and research directions discussed.…”

Interactive FACTS and demand response program as an incremental welfare consensus for maximizing wind power penetration

“…6 In the price-based programs, the electricity prices vary during the different periods leading to alter the consumption patterns of customers to use the electricity. 28 Besides, in the incentive-based DR program incentives are paid to the customers by the electricity utility to decrease their electricity consumption when the security of the system is at risk. 29 It should be stated that in this article, an effective incentive-based DR program is utilized to alleviate the congestion along with FACTS devices.…”

“…The exact value of LMP at each bus depends upon three factors, including the marginal cost of generation at the same bus, transmission congestion, and loss price. It should be noted that the calculations in the objective function (28) are performed regardless of the constraints related to the transmission network limitations.…”

Unified optimization model for energy management in sustainable smart power systems

“…Therefore, they are focusing on developing and implementing various algorithms, where they can manage the consumers' increasing LIST OF SYMBOLS AND ABBREVIATIONS: DSM, Demand side management; DR, Demand response; PAPR, Peak to average power ratio; HIF, Human interaction factor; LS, Load shedding; CPs, Consumer preferences; WM, Washing machine; DW, Dish washer Dy Dryer; EV, Electric vehicle; GHG, Greenhouse gas emission; TOU, Time of use electricity demand indirectly. 3,4 Consumers' electricity demand can be managed in two ways, ie, using demand response (DR) schemes, [5][6][7] and demand-side management (DSM). 8,9 DR is further classified into two groups, ie, direct control direct control of consumers' load and reshaping of consumers demand curve through dynamic pricing.…”