A novel demand control policy for improving quality of power usage in smart grid (original) (raw)
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—Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
Challenges in Demand Side Management in Smart Power Grid: A Review
A power system can be seen from two sides i.e. from supply side and from demand side. Now days, Demand Side Management (DSM) is emerging as important part of Smart Grid. In this paper, we study and analyse the challenges seen are load scheduling, peak load management, renewable energy sources connection with the grid, cost optimization etc. The load scheduling can be made by way of an accurate two-way communiqué which has its individual challenges in the power system. Also, few other issues are likely security and privacy which needs a special focus. A special attention has been added w.r.t environmental consciousness. All these challenges need an optimizing approach which can be done with the help of demand side algorithms and game theories.
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...
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.
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.
Energies
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 sc...
Optimizing Electricity Load and Cost for Demand Side Management in Smart Grid
October 2018, 2018
This paper proposes a mechanism for OELC (Optimizing Electricity Load and Cost) for smart grid. The load of every smart home is predicted one-hour prior to their actual usage. To fulfill PL (Predicted Load) of each consumer, multiple resources of electricity are considered, including RE (Renewable Energy) resources. Furthermore, cost to get PL from multiple resources is calculated. In proposed model 3-4 smart homes are grouped in the form of clusters. To reduce the amount of electricity bills, system also allows privileges to share electricity between adjacent smart homes within a cluster. To validate the OELC mechanism, extensive numerical simulations are conducted which shows a significant reduction in electricity load and cost for electricity consumers. In future, to enhance the functionality of OELC, security from cyber-attacks can be considered
Demand Side Management in Smart Grids Using a Repeated Game Framework
Demand-side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to offpeak times, the utility company charges consumers the differential pricing for using power at different times of the day. Consumers take into account these differential prices when deciding when and how much power to consume daily. Importantly, while consumers enjoy lower billing costs when shifting their power usage to offpeak times, they also incur discomfort costs due to the altering of their power consumption patterns. Existing works propose stationary strategies for the myopic consumers to minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested and foresighted consumers as a repeated energy scheduling game and prove that the stationary strategies are suboptimal in terms of long-term total billing and discomfort costs. Subsequently, we propose a novel framework for determining optimal nonstationary DSM strategies, in which consumers can choose different daily power consumption patterns depending on their preferences, routines, and needs. As a direct consequence of the nonstationary DSM policy, different subsets of consumers are allowed to use power in peak times at a low price. The subset of consumers that are selected daily to have their joint discomfort and billing costs minimized is determined based on the consumers power consumption preferences as well as on the past history of which consumers have shifted their usage previously. Importantly, we show that the proposed strategies are incentive compatible. Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.
Energies, 2016
In this paper, the problem of minimizing electricity cost and the peak system load in smart grids with distributed renewable energy resources is studied. Unlike prior research works that either assume all of the jobs are interruptible or power-shiftable, this paper focuses on more challenging scenarios in which jobs are non-interruptible and non-power-shiftable. In addition, as more and more newly-built homes have rooftop solar arrays, it is assumed that all users are equipped with a solar-plus-battery system in this paper. Thus, power can be drawn from the battery as needed to reduce the cost of electricity or to lower the overall system load. With a quadratic load-dependent cost function, this paper first shows that the electricity cost minimization problem in such a setting is NP-hard and presents a distributed demand-side management algorithm, called DDSM, to solve this. Experimental results show that the proposed DDSM algorithm is effective, scalable and converges to a Nash equilibrium in finite rounds.
Demand shaping to achieve steady electricity consumption with load balancing in a smart grid
The purpose of this paper is to study conflicting objectives between the grid operator and consumers in a future smart grid. Traditionally, customers in electricity grids have different demand profiles and it is generally assumed that the grid has to match and satisfy the demand profiles of all its users. However, for system operators and electricity producers, it is usually most desirable, convenient and cost effective to keep electricity production at a constant rate. The temporal variability of electricity demand forces power generators, especially load following and peaking plants to constantly manipulate electricity production away from a steady operating point. These deviations from the steady operating point usually impose additional costs to the system. In this work, we assume that the grid may propose certain incentives to customers who are willing to be flexible with their demand profiles which can aid in the allowance of generating plant to operate at a steady state. In this paper we aim to compare the tradeoffs that may occur between these two stakeholders. From the customers' perspectives, adhering to the proposed scheduling scheme might lead to some inconvenience. We thus quantify the customers inconvenience versus the deviations from an optimal set by the grid. Finally we try to investigate the trade-off between a grid load balancing objective and the customers' preferences.