Domestic Load Scheduling Using Genetic Algorithms

Soares, Ana; Gomes, Álvaro; Antunes, Carlos Henggeler; Cardoso, Hugo F.V.

doi:10.1007/978-3-642-37192-9_15

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…In DSM systems proposed in the literature, a mechanism is defined that, based on energy tariffs and data forecasts for future periods (e.g., photovoltaic power generation, devices future usage), is able to automatically and optimally schedule the home devices activities for future periods and to define the whole energy plan of users (i.e., when to buy and sell energy to the grid). The main goal of these solutions is to minimize the electricity costs while guaranteeing the users' comfort; this can be achieved through the execution of methods based on optimization models [12], [13] or heuristics, such as Genetic Algorithms [14] and customized Evolutionary Algorithms [15], which are used to solve more complex formulations of the demand management problem. Since…”

Section: Related Workmentioning

confidence: 99%

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A distributed demand-side management framework for the smart grid

Barbato

Capone

Chen

et al. 2015

Computer Communications

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This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users.In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomously its own scheduling, and (2) a hybrid approach, where each user must schedule all his appliances. We analyze numerically these two approaches, showing that they are characterized practically by the same performance level in all the considered grid scenarios.We model the proposed system using a non-cooperative game theoretical approach, and demonstrate that our game is a generalized ordinal potential one under general conditions. Furthermore, we propose a simple yet effective best response strategy that is proved to converge in a few steps to a pure Nash Equilibrium, thus demonstrating the robustness of the power scheduling plan obtained without any central coordination of the operator or the customers. Numerical results, obtained using real load profiles and appliance models, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to meet the growing energy demand.

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Section: Related Workmentioning

confidence: 99%

“…Similarly to (1), (3) and (4), constraints (14), (15) and (16) are used, respectively, to guarantee that each appliance a starts in exactly one time slot within the interval (ST ah , ET ah ), to define the daily power demand of house h and to upper-bound the demand of each house according to the supply limit π SL .…”

Section: Multiple-appliance Game Formulationmentioning

confidence: 99%

A distributed demand-side management framework for the smart grid

Barbato

Capone

Chen

et al. 2015

Computer Communications

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“…The point of reference can be used as an initiative to elaborate it more precisely. As explained in [45][46][47][48][49][50][51][52], nanogrid is considered the same as microgrid. In order to identify the two power structures individually, the similarities between the two need to be discussed at the first step.…”

Section: International Journal Of Photoenergymentioning

confidence: 99%

Variable Load Demand Scheme for Hybrid AC/DC Nanogrid

Rauf

Kalair

Khan

2020

International Journal of Photoenergy

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This paper addresses the use of nanogrid technology in resolving the issue of blanket load shedding for domestic consumers. This is accomplished by using different load management techniques and load classification and utilizing maximum solar energy. The inclusion of DC-based load in basic load and DC inverter load in regular load and scheduling of the burst load during the hours of maximum solar PV generation bring novelty in this work. The term “nanogrid” as a power structure remains ambiguous in various publications so far. An effort has been done in this paper to present a concise definition of nanogrid. Demand side load management is one of the key features of nanogrid, which enables end users to know major characteristics about their energy consumption during peak and off-peak hours. A microgrid option with nanogrid facility results in a more reliable system with overall improvement in efficiency and reduction in carbon emission. PV plants produce DC power; when used directly, the loss will automatically be minimized to 16%. The AC/DC hybrid nanogrid exhibits 63% more efficiency as compared to AC-only nanogrid and nearly 18% more efficiency as compared to DC-only nanogrid. Smart load shifting smoothens the demand curve 54% more adequately than during conventional load shifting. Simulation results show that real-time pricing is more economical than flat rate tariff for a house without DG, whereas flat rate results are more economical when DG are involved in nanogrids. 12.67%-21.46% saving is achieved if only flat rates are used for DG in nanogrid instead of real-time pricing.

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“…This process is repeated until a convergence criterion is satisfied. This same heuristic is also used in [73], which proposes a multi-objective problem to schedule shiftable devices with the goal of minimizing a utility function defined by the weighted sum of three terms: discomfort of the device schedule, cost of the energy consumed by the user and the penalty cost depending on the closeness of the peak demand to a given threshold and on the availability of energy in the power grid. As can be noted, the authors use a reward tariff, which rewards or penalizes the consumer based on the features of his demand.…”

Section: Deterministic Approach To Dsm For Individual Usersmentioning

confidence: 99%

Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

2014

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Abstract:The residential sector is currently one of the major contributors to the global energy balance. However, the energy demand of residential users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. With the massive introduction of renewable energy sources and the large variations in energy flows, also the residential sector is required to provide some flexibility in energy use so as to contribute to the stability and efficiency of the electric system. To address this issue, demand management mechanisms can be used to optimally manage the energy resources of customers and their energy demand profiles. A very promising technique is represented by demand-side management (DSM), which consists in a proactive method aimed at making users energy-efficient in the long term. In this paper, we survey the most relevant studies on optimization methods for DSM of residential consumers. Specifically, we review the related literature according to three axes defining contrasting characteristics of the schemes proposed: DSM for individual users versus DSM for cooperative consumers, deterministic DSM versus stochastic DSM and day-ahead DSM versus real-time DSM. Based on this classification, we provide a big picture of the key features of different approaches and techniques and discuss future research directions.

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Domestic Load Scheduling Using Genetic Algorithms

Cited by 20 publications

References 16 publications

A distributed demand-side management framework for the smart grid

A distributed demand-side management framework for the smart grid

Variable Load Demand Scheme for Hybrid AC/DC Nanogrid

Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

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