Optimal scheduling of distributed generations in microgrids for reducing system peak load based on load shifting

Ebrahimi, Javad; Abedini, Moein; Rezaei, Mohammad Mahdi

doi:10.1016/j.segan.2020.100368

Cited by 24 publications

(12 citation statements)

References 16 publications

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“…It utilises the load management techniques aimed to manipulate load profile curves by several ways: (1) peak clipping; (2) valley filling; (3) load shifting; (4) strategic conservation; (5) strategic load growth; (6) flexible load shape [10]. Although these load manipulation strategies were defined a few decades ago, the resent literature search [11][12][13][14][15] shows that they are still dominating in state-of-the-art DSM proposals and applications in both industrial and domestic sectors. The appropriate DSM technique from the list must be selected with respect to particular system parameters to ensure the most efficient operation of power supply sources.…”

Section: Domestic Demand Side Managementmentioning

confidence: 99%

“…The third scenario is a multi-objective optimisation combining the objective functions from the first and second scenarios. Ebrahimi et al [14] discussed an advanced particle swarm optimisation algorithm for load shifting control of domestic appliances. Almehizia et al [36] implemented a genetic algorithm technique to optimise the cost function indirectly reflecting electricity cost.…”

Section: Domestic Dsm Framework and Algorithmsmentioning

confidence: 99%

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Domestic demand-side management: analysis of microgrid with renewable energy sources using historical load data

et al. 2021

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This paper provides a high accuracy assessment of domestic demand-side management (DSM) approach in the context of distributed renewable energy sources (RES). To determine the potential of domestic DSM for households, a microgrid model of a typical UK residential estate was developed to simulate the impact of RES. The microgrid model comprises of 15 UK households with appropriate allocation of washing machines (WM), tumble dryers (TD) and dishwashers (DW) in accordance to the statistical data. In order to obtain a high accuracy result, the power consumption of the microgrid model utilises real historical high-resolution data of household energy consumption and RES generation. Thereafter, 40% of distributed wind and solar energy are implemented in the model to produce two individual scenarios. The operation of the white appliances in the model is controlled using a domestic DSM based on a load shifting algorithm. The primary criterion of the DSM considered in this paper is the reduction of energy feedback to the grid in order to decrease the utilisation of the grid and to reduce the transmission losses. The results obtained from the model simulation are compared to the baseline model and discussed with respect to the possible benefits of implementation of domestic DSM under the impact of RES. It has been shown that the self-consumption ratio of the microgrid operating under the DSM is increased by 3% for both scenarios. The model analysis provides highly realistic results which can be used for efficiency assessment of various load shifting methods.

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Section: Domestic Demand Side Managementmentioning

confidence: 99%

Section: Domestic Dsm Framework and Algorithmsmentioning

confidence: 99%

Domestic demand-side management: analysis of microgrid with renewable energy sources using historical load data

et al. 2021

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“…The results show the significant superiority of this algorithm in solving the optimal energy management problem compared to the classical GWO algorithm. MG optimal scheduling based on load shifting has been performed in reference [20] to improve the level of social welfare, utilizing a hybrid PSO algorithm. Reference [9] proposed a new evolutionary PSO (E‐PSO) algorithm for solving the MG economic dispatch problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal energy management of CHP‐based microgrid examining different types of district heating networks

Ahmadi

Rastegar

2023

IET Renewable Power Gen

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Heat recovery by combined heat and power (CHP) systems to provide heat load reduces the cost of microgrid (MG) energy management. However, heat transfer from CHP systems to consumers is limited due to the heat loss of pipelines. The amount of heat loss varies depending on pipeline material and environmental characteristics. Here, a day‐ahead energy management of CHP‐based microgrid, including various types of CHP systems to supply electrical and thermal loads, is performed. In this regard, the accurate model of heat transfer losses (HTL) through pipelines in the energy management problem is considered. Moreover, the optimal scheduling of different types of MGs in terms of HTL specifications is compared with each other. Evaluating the simulation results, it is inferred that accurate modeling of HTL in the energy management problem has a significant impact on the optimal scheduling and operation cost of MG. To solve the optimization problem, a new enhanced imperialist competitive algorithm (E‐ICA) is examined and validated. The E‐ICA method improves the efficiency of the classical ICA method to prevent getting stuck in local optimums by enhancing exploration and exploitation. The results of the energy management problem show E‐ICA superiority compared to other evolutionary algorithms.

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“…[20], DSM ensured the reduction of peak load demand in smart grids and a perspective on the interaction between non-ideal grids and LED lamps in residential buildings was presented in [21], which by extensive analysis of voltage harmonics, sustained abnormal voltage, supply frequency variations on LED lamps and showed its effect on the LED lighting program adopted worldwide. A model of hybrid Particle Swarm optimization algorithm with Sinusoidal and Cosine acceleration coefficient in [22] showed a reduction in peak load, reduction in consumer's energy bill and production cost savings in a microgrid.…”

Section: Introductionmentioning

confidence: 99%

A systemic model predictive control based on adaptive power pinch analysis for load shifting and shedding in an isolated hybrid energy storage system

Nyong-Bassey

A.M.

2022

Journal of Energy Systems

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This paper presents a novel systemic algorithm based on conservative power pinch analysis principles using a computationally efficient insight-based binary linear programming optimization technique in a model predictive framework for integrated load shifting and shedding in an isolated hybrid energy storage system. In a receding 24-hour predictive horizon, the energy demand and supply are integrated via an adaptive power grand composite curve tool to form a diagonal matrix of predicted hourly minimum and maximum energy constraints. The intgrated energy constraints must be satisfied recursively by the binary optimisation to ensure the energy storage’s state of charge only operates within 30% and 90%. Hence, the control command to shift or shed load is contingent on the energy storage state of the charge violating the operating constraints. The controllable load demand is shifted and/or shed to prevent any violations while ensuring energy supply to the most critical load without sacrificing the consumers' comfort. The proposed approach enhances efficient energy use from renewable energy supply as well as limits the use of the Hydrogen resources by a fuel cell to satisfy controllable load demands which can be shifted to periods in the day with excess renewable energy supply.

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Optimal scheduling of distributed generations in microgrids for reducing system peak load based on load shifting

Cited by 24 publications

References 16 publications

Domestic demand-side management: analysis of microgrid with renewable energy sources using historical load data

Domestic demand-side management: analysis of microgrid with renewable energy sources using historical load data

Optimal energy management of CHP‐based microgrid examining different types of district heating networks

A systemic model predictive control based on adaptive power pinch analysis for load shifting and shedding in an isolated hybrid energy storage system

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