Optimizing Electricity Load and Cost for Demand Side Management in Smart Grid (original) (raw)
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A New Scheme for Demand Side Management in Future Smart Grid Networks
This paper presents a new energy consumption scheduling scheme to enable Demand Side Management (DSM) in future Smart Grid Networks (SGNs). Electrical grid has been facing important challenges regarding quality and quantity to meet the increasing requirements of consumers. Environment friendly and economical generation along with efficient consumption through effective DSM in future SGNs will help in addressing most of these challenges because of integration of advanced information and communication technologies. In this work, we propose an autonomous energy scheduling scheme for household appliances in real-time to achieve minimum consumption cost and reduction in peak load. We assume that every user is equipped with smart meter which has an Energy Consumption Controlling (ECC) unit. Every ECC unit is connected with its neighbours through local area network to share power consumption information. ECC units run a distributed algorithm to minimize the peak load by transferring the shiftable loads from peak hours to off-peak hours. This ultimately minimizes the total energy consumption cost. Simulation results confirm that our proposed algorithm significantly reduces the peak load and energy consumption cost.
Demand Side Management for Smart Houses: A Survey
Sustainability
Continuous advancements in Information and Communication Technology and the emergence of the Big Data era have altered how traditional power systems function. Such developments have led to increased reliability and efficiency, in turn contributing to operational, economic, and environmental improvements and leading to the development of a new technique known as Demand Side Management or DSM. In essence, DSM is a management activity that encourages users to optimize their electricity consumption by controlling the operation of their electrical appliances to reduce utility bills and their use during peak times. While users may save money on electricity costs by rescheduling their power consumption, they may also experience inconvenience due to the inflexibility of getting power on demand. Hence, several challenges must be considered to achieve a successful DSM. In this work, we analyze the power scheduling techniques in Smart Houses as proposed in most cited papers. We then examine th...
Optimized Energy Consumption and Demand Side Management in Smart Grid
Smart Grid as a Solution for Renewable and Efficient Energy, 2000
This chapter reviews prevailing methodologies and future techniques to optimize energy consumption. It discerns that smart grid provides better tools and equipment to control and monitor the consumer load, and optimize the energy consumption. Smart grid is essentially composed of smart energy equipment, advance metering infrastructure and Phasor Measurement Units (Synchrophaors) that helps to achieve optimized energy consumption. The chapter also places focus on demand side management and optimized energy consumption scheduling; and establishes that both, the utilities, as well as the users can play a vital role in intelligent energy consumption and optimization. The literature review also reveals smart protection, self-healing systems and off-peak operation result in minimizing transmission and distribution losses, as well as optimizing the energy consumption.
Simulation Study for Optimized Demand Side Management in Smart Grid
2016
Simulation Study for Optimized Demand Side Management in Sm art Grid Smart grid is envisioned to meet the 21 st century energy requirements in a sophisticated manner with real time approach by integrating the latest digital co mmunications and advanced control technologies to the existing power grid. It will dynamicall y connect all the stake holders of smart grid through enhanced energy efficiency awareness cor ridor. Smart Homes (SHs), Home Energy Management Systems (HEMS) an d effect of home appliances scheduling in smart grid are now familiar research top ics in electrical engineering. Peak load management and reduction of Peak to Average Ratio (PAR) and associated methods are under focus of researchers since decades. These topics have got n w dimensions in smart grid environment. This dissertation aims at simulation study fo r e fective Demand Side Management (DSM) in smart grid environment. This work is mainly focused on optimal load scheduling for energy cost minimization and...
IEEE Access, 2021
The advancement of the smart grids (SGs) is enabling consumers to schedule home appliances to respond to demand response programs (DRs) offered by distribution system operators (DSOs). This way, not only will customers save money on their energy bills and be more comfortable, but the utility company will also be able to regulate peak-hour demand and reduce carbon emissions (CE). Designing an optimization scheme to reduce the electricity bill cost, peak-to-average ratio (PAR), CO 2 emission, wait time, and enhance the user comfort in terms of delay, luminance, and thermal comfort is not only the aim of this work but also the need of demand-side management. This research focuses on energy usage, scheduling, and management under the DR program of an electric utility, as well as renewable energy sources integration, i.e., solar energy (SE), thermal, controllable heat and power (CHP), and wind energy (WE). Moreover, the integration of renewable energy sources will reduce electricity bills and also lower the environmental impact of CE. In this context, a smart appliances scheduler and energy management controller (ASEMC) is proposed which is based on heuristic algorithms, i.e., genetic algorithm (GA), winddriven optimization (WDO), particle swarm optimization (PSO), bacterial foraging optimization (BFO) and our proposed hybrid of GA, PSO, and WDO (HGPDO) algorithm. The performance of the proposed scheme and heuristic algorithms is evaluated via simulations. Results show that in Scenario 1, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 25.7%, 36.39%, and 20.74%, respectively, while in Scenario 2, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 35.25%, 31.72%, and 36.30%, respectively. Furthermore, in Scenario 1, user comfort in terms of cumulative delay, indoor air freshness quality, thermal, and visual comfort improves by 26.77%, 3.28%, 13.33%, and 31.66%, whereas in Scenario 2, user comfort improves by 23.33%, 3.30%, 10%, and 45%, respectively. INDEX TERMS Smart grid, day ahead pricing, energy management, flat pricing, load scheduling, renewable energy, hybrid heuristic algorithms, demand response NOMENCLATURE Eex(t) Energy consumed from external power grid L sch bill Schedulable load energy bill cost L nsch bill Energy bill cost of non-schedulable load L sch Schedulable home appliances L nsch Non-schedulable home appliances ϕ(t) Total renewable energy sources at time t α Appliance operation start time
2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications, 2013
This paper presents an overview of home appliances scheduling techniques to implement demand side management in smart grid. Increasing demand of consumers have affected the power system badly as power generation system faces a number of challenges both in quality and quantity. Economical generation and efficient consumption can solve this problem in future smart grid as it is integrated with information and communication technologies. Smart grid has opportunities to employ different pricing schemes which help also in increasing the efficiency of appliances scheduling techniques. Optimal energy consumption scheduling minimizes the energy consumption cost and reduces the Peak-to-Average Ratio (PAR) as well as peak load demand. In this work, we discuss different energy consumption scheduling schemes that schedule the household appliances in real-time to achieve minimum energy consumption cost and reduce peak demand to shape the load curve.
A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment
Sustainability
The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artifi...
Various demand side management techniques and its role in smart grid-the state of art
International Journal of Electrical and Computer Engineering (IJECE), 2022
The current lifestyle of humanity relies heavily on energy consumption, thus rendering it an inevitable need. An ever-increasing demand for energy has resulted from the increasing population. Most of this demand is met by the traditional sources that continuously deplete and raise significant environmental issues. The existing power structure of developing nations is aging, unstable, and unfeasible, further prolonging the problem. The existing electricity grid is unstable, vulnerable to blackouts and disruption, has high transmission losses, low quality of power, insufficient electricity supply, and discourages distributed energy sources from being incorporated. Mitigating these problems requires a complete redesign of the system of power distribution. The modernization of the electric grid, i.e., the smart grid, is an emerging combination of different technologies designed to bring about the electrical power grid that is changing dramatically. Demand side management (DSM) allow customers to be more involved in contributors to the power systems to achieve system goals by scheduling their shiftable load. Effective DSM systems require the participation of customers in the system that can be done in a fair system. This paper focuses primarily on techniques of DSM and demand responses (DR), including scheduling approaches and strategies for optimal savings.
A novel pricing mechanism for demand side load management in smart grid
—This paper proposes a novel demand response (DR) mechanism based on real time electricity prices with the objective of cost reduction. The novelty of the proposed mechanism lies in the concept that electricity sub-prices are calculated based on the fraction of energy consumed by each unit/home. While, in traditional residential energy management programs, one DR signal applies to each associated unit without considering low, medium or high energy consumers. The proposed mechanism calculates the electricity prices for each individual unit based on which the electricity is calculated. Furthermore, the proposed mechanism is designed in such a way that it is equally feasible for all types of DR programs being used for demand side energy management. To assess the feasibility and practical applicability of the proposed mechanism, extensive simulations are conducted. The simulation results verify that the mechanism is efficient in calculating sub-prices without violating the utility constraint (i.e., the net cost remains same in both traditional and proposed mechanisms). Index Terms—smart grid; demand side management; home energy management; renewable energy source; energy storage system; real time pricing; genetic algorithm; binary particle swarm optimization; wind driven optimization.
Sustainable Cities, 2017
Demand side management (DSM) is one of the most interesting areas in smart grids, and presents households with numerous opportunities to lower their electricity bills. There are many recent works on DSM and smart homes discussing how to keep control on electricity consumption. However, systems that consider minimization of peak load and cost simultaneously for a residential area with multiple households have not received sufficient attention. This study, therefore, proposes an intelligent energy management framework that can be used to minimize both electrical peak load and electricity cost. Constraints , including daily energy requirements and consumer preferences are considered in the framework and the proposed model is a multi-objective mixed integer linear programming (MOMILP). Simulation results for different scenarios with different objectives verified the effectiveness of the proposed model in significantly reducing the electricity cost and the electrical peak load.