A novel peak shaving algorithm for islanded microgrid using battery energy storage system

Uddin, Moslem; Romlie, Mohd Fakhizan; Abdullah, Muhammad; Tan, Chee Keong; Shafiullah, GM; Bakar, Abu

doi:10.1016/j.energy.2020.117084

Cited by 122 publications

(57 citation statements)

References 39 publications

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“…In the Ref. [24], the authors proposed a decision-tree-based peak shaving algorithm for isolated MGs using BESSs. A multi-objective UC and economic dispatch model considering ESS sizing has also been presented [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

Lee

Kim

2021

Energies

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As renewable penetration increases in microgrids (MGs), the use of battery energy storage systems (BESSs) has become indispensable for optimal MG operation. Although BESSs are advantageous for economic and stable MG operation, their life degradation should be considered for maximizing cost savings. This paper proposes an optimal BESS scheduling for MGs to solve the stochastic unit commitment problem, considering the uncertainties in renewables and load. Through the proposed BESS scheduling, the life degradation of BESSs is minimized, and MG operation becomes economically feasible. To address the aforementioned uncertainties, a scenario-based method was applied using Monte Carlo simulation and the K-means clustering algorithm for scenario generation and reduction, respectively. By implementing the rainflow-counting algorithm, the BESS charge/discharge state profile was obtained. To formulate the cycle aging stress function and examine the life cycle cost (LCC) of a BESS more realistically, the nonlinear cycle aging stress function was partially linearized. Benders decomposition was adopted for minimizing the BESS cycle aging, total operating cost, and LCC. To this end, the general problem was divided into a master problem and subproblems to consider uncertainties and optimize the BESS charging/discharging scheduling problem via parallel processing. To demonstrate the effectiveness and benefits of the proposed BESS optimal scheduling in MG operation, different case studies were analyzed. The simulation results confirmed the superiority and improved performance of the proposed scheduling.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

Lee

Kim

2021

Energies

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“…Such an approach is rather important because previous studies have primarily dealt with improving the performance of single systems separately (eg, ESS or PV) which is likely suboptimal. [26][27][28][29][30][31][32][33][34] The system working principle is made of two modules: a forecasting module and a scheduling module. The forecasting module uses real-time weather information and is composed of two deep neural networks (DNN), forecasting models for EHP energy consumption and PV energy generation.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, similar studies have often relied on traditional prediction techniques, which have potential limitations (eg, in terms of accuracy) in modelling complex behaviour, to predict/forecast building energy demand or PV energy generation. 29,30,[33][34][35][36]41 To overcome such limitations and improve prediction accuracy, we employ the DNN algorithm to forecast PV energy generation and EHP energy demand and subsequently determine the ESS charging/discharging schedules. [45][46][47][48][49][50] Another critical aspect of our study is related to the parameters used in forecasting EHP energy consumption and PV energy generation.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

et al. 2021

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The use of photovoltaic (PV) systems has drawn attention as a solution to reduce the dependence on fossil fuel for building energy needs. Moreover, incorporating energy storage systems (ESSs) in PV systems can optimise electric energy costs by increasing dependency on PV-generated energy during electric peak load times. However, current ESSs have limited capacities making it difficult to fully maximise PV-generated energy. We propose a novel integrated energy-efficient system for PV, ESS and electric heat pump (EHP) that maximises the usage of PV energy, optimises ESS usage and reduces EHP energy consumption costs. The components of the proposed integrated system are linked with a deep learning (DL)-based algorithm that forecasts PV energy generation and energy demand of the EHP. The proposed system schedules the charging/discharging time of ESSs depending on peak load times, the forecasted EHP electric demand, and PV-generated energy. The data used were collected for 10 months from a retail shop equipped with an EHP and ESS. We found that the developed DL-based forecasting models for PV and EHP are accurate and reliable (ie, R 2 above 0.95). Also, the results show that the proposed integrated energy-efficient PV-ESS-EHP system saves 12% of the total annual electric costs, which corresponds to 1 285 291 Won. The proposed system ensures an efficient method to maximise PV-generated energy resulting in reduced dependency on fossil fuels for building energy needs.

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“…Barzak and Hosseni 27 modelled peak load shaving for residential building using the shortest path optimization method, followed by a method for real-time scheduling of the storage system. A novel, decision-tree-based peak-shaving algorithm was proposed by Uddin et al 28 , tested in a real islanded microgrid system, for various load conditions. The cost-benefit analysis showed that the overall revenue from the proposed system is 1.84 times of the capital investment.…”

Section: Introductionmentioning

confidence: 99%

Energy management and techno‐economic assessment of a predictive battery storage system applying a load levelling operational strategy in island systems

et al. 2020

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In the present study, a predictive battery energy storage system (BESS) for application in geographical non-interconnected islands with high renewable energy penetration is proposed, capable of performing load levelling. The system under consideration is composed of diesel and heavy oil generators, a photovoltaic farm and a small wind turbine. The proposed solution integrates machine learning (ML) methods for the forecasting of load and intermittent solar and wind power productions, alongside a custom scheduling algorithm, which calculates the necessary BESS setpoints that accomplish the desired levelling effect. An important feature of the scheduling algorithm is that the charge and discharge energy amounts of each day are by design equal and independent of the forecasts' accuracy. This aspect enables economic investigations to identify the appropriate BESS capacity for the particular system, taking also into account the battery's capacity degradation. The overall system is modelled and simulated utilizing the open-source languages Python and Modelica. Simulations presented a 9.8% peak-to-mean ratio (PMR) reduction of the thermal plant's load. Furthermore, economic investigations estimated a marginal BESS cost of 287.1 €/kWh revealing the financial viability of the proposed integrated system, in at least the case of geographical islands.

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A novel peak shaving algorithm for islanded microgrid using battery energy storage system

Cited by 122 publications

References 39 publications

Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

Energy management and techno‐economic assessment of a predictive battery storage system applying a load levelling operational strategy in island systems

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