Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids

Kumar, Ramesh; Raghav, L. Phani; Raju, D. Koteswara; Singh, Arvind R.

doi:10.1016/j.apenergy.2021.117466

Cited by 46 publications

(14 citation statements)

References 53 publications

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“…Research has also been conducted on the interoperability of multiple MGs. In [20], a stochastic‐based framework is proposed for grid‐connected MGs to solve the EMS problem in the presence of multiple demand‐side management schemes. The method utilizes a flexible price elasticity technique and a nature‐inspired metaheuristic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Optimizing electricity demand scheduling in microgrids using deep reinforcement learning for cost‐efficiency

Xiong

Guo²,

Zhang³

et al. 2023

IET Generation Trans & Dist

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Renewable energy sources (RES) are increasingly being developed and used to address the energy crisis and protect the environment. However, the large-scale integration of wind and solar energy into the power grid is still challenging and limits the adoption of these new energy sources. Microgrids (MGs) are small-scale power generation and distribution systems that can effectively integrate renewable energy, electric loads, and energy storage systems (ESS). By using MGs, it is possible to consume renewable energy locally and reduce energy losses from long-distance transmission. This paper proposes a deep reinforcement learning (DRL)-based energy management system (EMS) called DRL-MG to process and schedule energy purchase requests from customers in real-time. Specifically, the aim of this paper is to enhance the quality of service (QoS) for customers and reduce their electricity costs by proposing an approach that utilizes a Deep Q-learning Network (DQN) model. The experimental results indicate that the proposed method outperforms commonly used real-time scheduling methods significantly.

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Section: Related Workmentioning

confidence: 99%

Optimizing electricity demand scheduling in microgrids using deep reinforcement learning for cost‐efficiency

Xiong

Guo²,

Zhang³

et al. 2023

IET Generation Trans & Dist

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“…Whenever the intermittent renewable energy sources are inadequate to supply consumer loads, the DG units in the MG network, such as MT, FC, and diesel generation unit, compensates the balance generation and optimizes the overall operating costs. The system data related to generation and load forecasts, market prices, and DER bids, along with nonconvex cost coefficients, are taken from References 19 and 21.…”

Section: Mathematical Modeling Of a Microgridmentioning

confidence: 99%

“…The prior research on the assimilation of utility‐induced DSM tactics into the EMS problem is focused on load reduction 4 and strategic conversion 5 . In contrast to these works, the flexible load shaping strategy is introduced in recent research 27 to enhance utility and MG energy exchange costs. The overall operating cost of grid‐connected MG is significantly reduced with DSM participation.…”

Section: Introductionmentioning

confidence: 99%

Optimal day ahead energy consumption management in grid‐connected microgrids

Raghav

Kumar

Raju

et al. 2021

Intl J of Energy Research

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The day-ahead scheduling of microgrids in the presence of nondispatchable distributed generators (DGs) is a challenging task for microgrid operators. The valve point loading problem of distributed generators and its effect on inputoutput characteristics is not extensively covered in the published scientific literature on microgrids. In this research work, the day-ahead scheduling problem of microgrids is formulated in the presence of DGs with a nonconvex cost function. First, the flexible load-shaping based demand-side management strat-

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“…Various algorithms have been used to solve such models. For example, in [17], the problem of optimal microgrid management based on the flexible load shaping DSM strategy as well as the price-based and incentive-based demand response programs is solved through the Black Widow Optimization algorithm. In [18], the fmincon interior-point algorithm is used for the optimal operation of the microgrid consisting of photovoltaics with a diesel generator under the probabilistic scenario.…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony

et al. 2022

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An important issue in power systems is the optimal operation of microgrids with demand-side management. The implementation of demand-side management programs, on the one hand, reduces the cost of operating the power system, and on the other hand, the implementation of such programs requires financial incentive policies. In this paper, the problem of the optimal operation of microgrids along with demand-side management (DSM) is formulated as an optimization problem. Load shifting is considered an effective solution in demand-side management. The objective function of this problem is to minimize the total operating costs of the power system and the cost of load shifting, and the constraints of the problem include operating constraints and executive restrictions for load shifting. Due to the dimensions of the problem, the simultaneous combination of a genetic algorithm and an ABC is used in such a way that by solving the OPF problem with an ABC algorithm and applying it to the structure of the genetic algorithm, the main problem will be solved. Finally, the proposed method is evaluated under the influence of various factors, including the types of production units, the types of loads, the unit uncertainty, sharing with the grid, and electricity prices all based on different scenarios. To confirm the proposed method, the results were compared with different algorithms on the IEEE 33-bus network, which was able to reduce costs by 57.01%.

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Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids

Cited by 46 publications

References 53 publications

Optimizing electricity demand scheduling in microgrids using deep reinforcement learning for cost‐efficiency

Optimizing electricity demand scheduling in microgrids using deep reinforcement learning for cost‐efficiency

Optimal day ahead energy consumption management in grid‐connected microgrids

Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony

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