A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators

Hajibandeh, Neda; Shafie‐khah, Miadreza; Osório, Gerardo J.; Aghaei, Jamshid; Catalão, João P.S.

doi:10.1016/j.apenergy.2017.12.076

Cited by 49 publications

(24 citation statements)

References 31 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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“…During recent years, DR has regained significant attention as a potential solution for tackling the economic, technical and environmental challenges of power grids [9][10][11]. In [12], a financial approach to incentivize customers to take part in the DR program is applied.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

“…The economic costs of power interruptions in Lebanon are calculated in [35,36], a willingness-to-pay DR mechanism based on locational area with transmission constraints is presented around income statistics and utilizes a state-space approach to analyze the possibility of altering prices by DR. The transmission thermal flow limits are taken into account in (11).…”

Section: Constraintsmentioning

confidence: 99%

Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme

et al. 2019

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Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer’s disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.

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“…The uncertainties related to outputs of renewables, day-ahead and real-time electricity prices are considered. A multi-objective DR scheduling for a system with high penetration of renewables is studied in [15]; however, comfort levels are not considered. The authors in [16] propose a general stochastic optimization framework for the energy scheduling in CBs considering wind power uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market

et al. 2019

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Demand response has been implemented by distribution system operators to reduce peak demand and mitigate contingency issues on distribution lines and substations. Specifically, the campus-based commercial buildings make the major contributions to peak demand in a distribution system. Note that prior works neglect the consumers' comfort level in performing demand response, which limits their applications as the incentives are not worth as compared to the loss in comfort levels for most time. Thus, a framework to comprehensively consider both operating costs and comfort levels is necessary. Moreover, distributed energy resources are widely deployed in commercial buildings such as roof-top solar panels, plug-in electric vehicles, and energy storage units, which bring various uncertainties to the distribution systems, i.e., (i) output of renewables; (ii) electricity prices; (iii) arrival and departure of plug-in electric vehicles; (iv) business hour demand response signals and (v) flexible energy demand. In this paper, we propose an optimal demand response framework to enable local control of demand-side appliances that are usually too small to participate in a retail electricity market. Several typical small demand side appliances, i.e., heating, ventilation, and air conditioning systems, electric water heaters and plug-in electric vehicles, are considered in our proposed model. Their operations are coordinated by a central controller, whose objective is to minimize the total cost and maximize the customers' comfort levels for multiple commercial buildings. A scenario-based stochastic programming is leveraged to handle the aforementioned uncertainties. Numerical results based on the practical data demonstrate the effectiveness of the proposed framework. In addition, the trade-off between the operation costs of commercial buildings and customers' comfort levels is illustrated.

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A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators

Cited by 49 publications

References 31 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

Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme

Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market

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