Optimal residential community demand response scheduling in smart grid

Nan, Sibo; Zhou, Ming; Li, Gengyin

doi:10.1016/j.apenergy.2017.06.066

Cited by 262 publications

(97 citation statements)

References 29 publications

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“…As such, demand side methods are also needed. Demand response (DR) is one such method, [2][3][4][5] where electricity demand is modulated to better match the supply curve, rather than the other way around.…”

Section: Discussionmentioning

confidence: 99%

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

et al. 2019

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Summary The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation.

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Section: Discussionmentioning

confidence: 99%

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

et al. 2019

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“…The application of optimisation-based DSM techniques in electrical power systems has been widely researched topic, with models relating to electrically powered resources being recently proposed in [26], [27] and [28]. However, since these approaches focus on electrical energy systems, they do not include multi-energy technologies that unlock additional flexibility and enable IDSM, such as co-generation.…”

Section: A Related Literaturementioning

confidence: 99%

“…However, since these approaches focus on electrical energy systems, they do not include multi-energy technologies that unlock additional flexibility and enable IDSM, such as co-generation. It is worth mentioning that the DSM model in [27] covers a range of flexible demand characteristics captured by the model presented in this paper but is only focused on a single primary energy vector.…”

Section: A Related Literaturementioning

confidence: 99%

Generalised control-oriented modelling framework for multi-energy systems

2019

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A requirement for new sources of flexibility in meeting energy demand and the growing interest in the interaction between energy sectors have drawn attention to multienergy systems, which can offer numerous benefits, e.g. providing flexibility to counteract the intermittency of renewable generation and increase energy efficiency. In order to maximise these benefits, advanced control methods are required. The model predictive control methodology is a promising candidate for such applications as it is capable of incorporating economic and operational objectives whilst respecting various technical, regulatory and environmental constraints. In order to implement such a control strategy effectively, it is necessary to develop appropriate system models. This paper presents a novel generalised modelling framework for multi-energy systems that is particularly well suited, though not limited to, predictive control applications. The proposed approach is capable of representing energy converter arrangements of arbitrary complexity containing multiple energy vectors, as well as multi-directional energy flow, multi-generation and multi-mode devices, a wide range of controllable producers/consumers, energy storage and flexible loads. The effectiveness of the approach is demonstrated using a representative case study based on three buildings at the University of Manchester where the developed model is incorporated into a model predictive control scheme. The simulation results show how the controller minimises the cost of purchasing energy whilst satisfying operational constraints, and managing various types of flexible demand.

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“…Waitng parameter (ζ) 1 1.01 In this section, we formulate an optimization problem that minimizes the total electricity consumption bill while stabilizing the user ease with minimum waiting cost. The purpose of this model is to shift the energy consumption schedules within the residential neighbourhood in order to achieve optimum savings.…”

Section: Impact Of Control Parameter On Scheduling ζ Amentioning

confidence: 99%

“…The growing energy demand has led the researchers to establish a new energy management mechanism or to find alternate energy resources. For this purpose, the utility is transforming its infrastructure into smart grids (SGs) by using bi-directional communication technologies to make intelligent decisions [1,2]. For bi-directional flow of information between energy generators and users, a reliable and effective communication infrastructure is required in SGs [3,4].…”

Section: Introductionmentioning

confidence: 99%

Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

et al. 2018

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Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort..

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Optimal residential community demand response scheduling in smart grid

Cited by 262 publications

References 29 publications

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

Generalised control-oriented modelling framework for multi-energy systems

Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

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