Optimal Stochastic Day-Ahead Power Management of Hybrid AC-DC Microgrids

Due to the reappearance of DC loads in electrical systems and advanced improvement in energy storage systems (batteries) and environment-friendly properties of photovoltaics as a green energy supply, DC architecture is considered as a new solution for next-generation power distribution systems. Hybrid AC-DC microgrids (MG) can take advantage of DC and AC flows in a smart distribution system. The best strategy for the optimal operation of hybrid MGs is to minimize the converting energy between AC and DC sides such that DC loads are provided by photovoltaics, fuel cells, and the stored energy in batteries and AC loads are satisfied by AC-based sources including wind turbines (WTs) and diesel generators (DEs). Accordingly, this paper aims to scrutinize an optimal green power management strategy for hybrid AC-DC MGs from an economic viewpoint while considering photovoltaics as a prior source for the DC side and wind turbines for the AC side. Moreover, the uncertainties of renewable energy sources (RESs), DC and AC loads, and the correlation among them are investigated using the unscented transformation method

Effect of Battery Degradation on the Probabilistic Optimal Operation of Renewable-Based Microgrids

In order to maximize the use of renewable-based distributed generators (DGs), in addition to dealing with the effects of the inherent power management uncertainties of microgrids (MGs), applying storage devices is essential in the electrical system. The main goal of this paper is to minimize the total operation cost as well as the emissions of MG energy resources, alongside the better utilization of renewable energy sources (RES) and energy storage systems. The uncertainties of wind speed, solar irradiation, market price and electrical load demand are modeled using reduced unscented transformation (RUT) method. Simulation results reveal that, as expected, by increasing the battery efficiency, the achievable minimum daily operational cost of the system is reduced. For example, with 93% battery efficiency, the operational cost equals EUR 9200, while for an efficiency of 97%, the achievable minimum daily operational cost is EUR 8900. Moreover, the proper economic/environmental performance of the suggested approach, which contributes to the possibility of selecting a compromise solution for the MG operator in accordance with technical and economic constraints, is justified.</p

Environmental–Economic Analysis of Multi-Node Community Microgrid Operation in Normal and Abnormal Conditions—A Case Study of Indonesia

first_pagesettingsOrder Article ReprintsOpen AccessArticleEnvironmental–Economic Analysis of Multi-Node Community Microgrid Operation in Normal and Abnormal Conditions—A Case Study of Indonesiaby Mahshid Javidsharifi 1,*,Najmeh Bazmohammadi 1ORCID,Hamoun Pourroshanfekr Arabani 2,Juan C. Vasquez 1 andJosep M. Guerrero 1ORCID1Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark2Division of Industrial Electrical Engineering &amp; Automation, Lund University, 221 00 Lund, Sweden*Author to whom correspondence should be addressed.Sustainability 2023, 15(24), 16625; https://doi.org/10.3390/su152416625Submission received: 17 October 2023 / Revised: 27 November 2023 / Accepted: 29 November 2023 / Published: 7 December 2023(This article belongs to the Special Issue Smart Grids and Microgrids in Smart Cities: Operation, Control, Protection and Security)Downloadkeyboard_arrow_down Browse Figures Versions NotesAbstractThis paper presents a comprehensive analysis of the operation management of a multi-node community microgrid (MG), emphasizing power flow constraints and the integration of photovoltaic (PV) and battery systems. This study formulates MG operation management as a multi-objective optimal power flow problem, aiming to minimize costs (maximize profits) and emissions simultaneously. The multi-objective particle swarm optimization (MPSO) method is employed to tackle this complex optimization challenge, yielding a Pareto optimal front that represents the trade-offs between these conflicting objectives. In addition to the normative operation scenarios, this research investigates the robustness of the MG system in the face of abnormal situations. These abnormal scenarios include damage to the PV system, sudden increases in the MG load, and the loss of connection to the main electricity grid. This study focuses on Lombok Island, Indonesia as a practical case study, acknowledging the ongoing efforts to implement the community MG concept in this region. It is observed that when the access to the electricity grid is limited, the energy not served (ENS) increases to 2.88 MWh. During the fault scenario in which there is a 20% increase in the hourly load of each MG, a total of 4.5 MWh ENS is obtained. It is concluded that a resilient operation management system is required to ensure a consistent and reliable energy supply in community MGs in the face of disruptions

Quantifying the Impact of Different Parameters on Optimal Operation of Multi-Microgrid Systems

The multi-objective optimal power management of multi-microgrid systems is solved in this paper. Minimizing the total cost and emission of the system are considered as the objective functions. The multi-objective particle swarm optimization algorithm is applied on a multi-microgrid system that consists of four microgrids each includes diesel generators, wind turbines, photovoltaic units, battery, and local loads. The multi-microgrid system can exchange power with the electricity grid. Moreover, the adjacent microgrids in the multi-microgrid system can share power with each other. The impact of the variation of battery charging and discharging efficiency, the electricity price, the capacity of diesel generators and renewable-based units, the maximum exchangeable power between the multi-microgrid system and the electricity grid and the power sharing among adjacent microgrids on day-Ahead units' scheduling of multi-microgrid are evaluated through sensitivity analysis in simulation results.</p

Demand response planning for day-ahead energy management of CHP-equipped consumers

Due to the growing importance of demand response program (DRP) in demand side management in power systems as well as increasing employment of combined heat and power (CHP) units, the issue of energy management of large consumers equipped with CHP units in the presence of a DRP based on the day-ahead electricity price has been studied in this paper. To solve the considered non-convex and non-linear energy management problem, particle swarm optimization (PSO) algorithm has been used. Also, given the importance of the effect of uncertainties on the planning and operation of units in the energy management, the unscented transformation (UT) method is used for modeling uncertainties related to electricity prices and the amount of electric and thermal loads. In the applied DRP, the consumers can shift a percentage of their load from higher-price hours to lower-price hours to reduce operating costs. No load-shedding is considered in the problem formulation. The consumer energy system consists of two CHP units, one electrical unit, one thermal unit, and a heat buffer tank (HBT) for the storage of surplus thermal energy. The consumer can also buy electricity from the main electricity grid to supply the demanded load based on the price of electricity. The simulation results show that the application of the suggested DRP reduces the operational cost.</p

Stochastic Optimal Strategy for Power Management in Interconnected Multi-Microgrid Systems

A novel stochastic strategy for solving the problem of optimal power management of multi-microgrid (MMG) systems is suggested in this paper. The considered objectives are minimizing the total cost and emission of the system. The suggested algorithm is applied on a MMG consisting of four microgrids (MG), each including fossil fuel-based generator units, wind turbine (WT), photovoltaic (PV) panel, battery, and local loads. The unscented transformation (UT) method is applied to deal with the inherent uncertainties of the renewable energy sources (RES) and forecasted values of the load demand and electricity price. The proposed algorithm is applied to solve the power management of a sample MMG system in both deterministic and probabilistic scenarios. It is justified through simulation results that the suggested algorithm is an efficient approach in satisfying the minimization of the cost and the environmental objective functions. When considering uncertainties, it is observed that the maximum achievable profit is about 23% less than that of the deterministic condition, while the minimum emission level increases 22%. It can be concluded that considering uncertainties has a significant effect on the economic index. Therefore, to present more accurate and realistic results it is essential to consider uncertainties in solving the optimal power management of MMG system

PV-Powered Base Stations Equipped by UAVs in Urban Areas

Recently, the application of unmanned aerial vehicles (UAVs) to support the base stations in cellular telecommunication networks attracts attentions. UAV-assisted base stations can provide the extra users' demand in extreme and/ or unpredictable situations such as Olympic Games to avoid extra cost of installing ground base stations. In this paper, a PV-battery power system is presented to supply UAV-assisted base stations in cellular telecommunication networks in urban areas to prevent environmental issues as well as to reduce the cost of fulfilling the energy demand. First, the energy consumption profile of the batteries of UAVs is estimated. Afterwards, the impact of the PV system sizing and battery capacity are studied based on sensitivity analysis

Optimum Sizing of Photovoltaic and Energy Storage Systems for Powering Green Base Stations in Cellular Networks

Satisfying the mobile traffic demand in next generation cellular networks increases the cost of energy supply. Renewable energy sources are a promising solution to power base stations in a self-sufficient and cost-effective manner. This paper presents an optimal method for designing a photovoltaic (PV)-battery system to supply base stations in cellular networks. A systematic approach is proposed for determining the power rating of the photovoltaic generator and battery capacity from a technical and economical point of view in order to minimize investment cost as well as operational expenditure, while the power autonomy of the PV-battery system is maximized in a multi-objective optimization framework. The proposed method is applied to optimally size a photovoltaic-battery system for three cases with different availability of solar power to investigate the effect of environmental conditions. Problem-solving using the proposed approach leads to a set of solutions at different costs versus different levels of power autonomy. According to the importance of each criterion and the preference of decision-makers, one of the achieved solutions can be selected for the implementation of the photovoltaic-battery system to supply base stations in cellular network