An Improved Optimal Sizing Methodology for Future Autonomous Residential Smart Power Systems

This paper presents a methodology for the capacity optimization of a residential stand-alone microgrid employing solar photovoltaics (PVs), wind turbines (WTs), battery energy storage system (BESS), and diesel generator (DG) for electric power generation and typical house power demand and electrical vehicle (EV) power demand as load power demand. The cost function is formulated based upon life cycle cost, dump energy cost, and green-house gases (GHG) emissions. The optimization problem involves a variety of realistic constraints from hybrid renewable power generation, BESS, diesel generation system, and EVs. The sizing problem is formulated and solved for different possible topologies of renewable energy sources (PV and WT), BESS, and DG, and a comparison based upon cost per unit, GHG emissions, and emission reduction benefit cost is presented.

Microgrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixed-integer linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it’s impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it’s effectiveness.

With the rapid development of environmental technologies such as renewable energy, smart grid, and electric power transportation, future power generation and power supply will show new features. The design of energy consumption characteristics of modern power system is more flexible and easy to control, which will also affect the scale of power generation system. This paper presents a combined capacity optimization method for a typical independent microgrid including solar photovoltaic, wind turbines, diesel generators and battery energy storage system. The mathematical models of photovoltaic, wind turbines, diesel generation system, battery energy storage system and electric vehicle charging loads are developed to improve the capacity optimization method. Research objectives include: 1) minimizing costs; 2) reducing greenhouse gas emissions; 3) reducing waste energy. This research can provide strong support for decision-making, analysis and strategy-making of multi-agent joint microgrid capacity.

This paper analyzed the influence of wind power fluctuations in grid frequency of a stand-alone microgrid that is hybrid generation system with diesel generator, wind turbine, and Battery Energy Storage System (BESS). The existing island area power system consists of only diesel generators. So the grid frequency can be controllable from load change. But hybrid generation system with Renewable Energy Sources (RES) such as wind energy that has the intermittent output can bring power quality problems. BESS is one of the ways to improve the intermittent output of the RES. In this paper, we analyzed the role of BESS in a stand-alone microgrid. We designed a modelling of wind power system with squirrel-cage induction generator, diesel power system with synchronous generator, and BESS using transient analysis program PSCAD/EMTDC. And we analyzed the variation of the grid frequency according to the output of BESS.

Microgrids (MGs) are gaining popularity due to their ability to provide reliable and resilient power supply, especially when integrated with renewable energy sources (RESs) and battery energy storage systems (BESS). Reliability is a critical factor for MG owners and policy makers. However, existing reliability indices such as loss of load expectation (LOLE) and expected energy not supplied (EENS) may not offer a comprehensive understanding of MG reliability and resiliency. To address this gap, this paper proposes three new indices for MGs to provide supplementary information on the performance of RESs: the Microgrid Resiliency Index (MRI), the Microgrid Renewable Energy Availability Index (MREAI), and the Microgrid Renewable Energy Energy Index (MREEI). The MRI assesses the MG’s ability to recover from interruptions, providing insights into its resiliency. The MREAI and MREEI offer additional information beyond LOLE and EENS, specifically highlighting the contribution of RESs to the availability and energy losses in the MG. These indices enable a more comprehensive assessment of MG reliability. The formulation for calculation of the indices are provided and applied to a MG with integrated solar photovoltaic (PV), wind turbine (WT), and BESS components. To evaluate the effectiveness of the proposed indices in providing supplementary information on resiliency and the contribution of RESs, various scenarios are examined. These scenarios include the impact of using BESS, changes in RES availability, and variations in RES output. The results demonstrate that the proposed indices effectively capture the contribution of RES to MG reliability and offer valuable insights for decision-making related to RES installation. Also, by integrating BESS, the contribution of RESs to outage hours and lost energy is effectively decreased from 89.41% and 87.41% to 32.69% and 28.94% respectively. Additionally, results highlight the substantial enhancement in the resiliency of the MG, which witnessed an impressive 68.43% improvement.

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges.

This paper presents an optimization model to minimize the fuel cost and CO2 emision on university campuses using an hybrid power system (HPS). The HPS is made up of solar photovoltaic (PV), diesel generator (DG), wind turbine (WT) and battery energy storage system (BESS). Two university campuses are used as case study to investigate the efficiency of the proposed HPS. The objective function is formulated such that each campus load is met by the renewable energy source (RES) when available and the DG only swicthes on when the output of the RES is not eneough to meet the load. The resulting non linear optimization problem is solved using a function in MATLAB called “quadprog”. The results of the simulation are analyzed and compared with the base case in which the DG is used exclusively to meet the entire load. The results show the effectiveness of the optimized HPS in saving fuel when compared to the base case and reflect the effects of seasonal variations in fuel costs.

Imposing versus Enacting Commitments for the Long‐Term Energy Transition: Perspectives from the Firm

Microgrids with hybrid renewable energy sources are increasing and it is a promising solution to electrify remote areas where distribution network expansion is not feasible or not economical. Standalone microgrids with environment-friendly hybrid energy sources is a cost-effective solution that ensures system reliability and energy security. This paper determines the optimal capacity, energy dispatching and techno-economic benefits of standalone microgrid in remote area in Tamilnadu, India. Microgrids with hybrid energy sources comprising photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS) and diesel generator (DG) are considered in this paper. Various case studies are implemented with hybrid energy sources and for each case study a comparative analysis of techno-economic benefits is demonstrated. Eight different configurations of hybrid energy sources are modeled with renewable fractions of 50%, 60%, 65%, and 100%, respectively. The optimization analysis is carried out using Hybrid Optimization Model for Electric Renewable (HOMER) software. Impact of demand response is also demonstrated on energy dispatching and techno-economic benefits. Simulation results are obtained for the optimal capacity of PV, WT, DG, converter, and BESS, charging/discharging pattern, state of charge (SOC), net present cost (NPC), cost of energy (COE), initial cost, operation cost, fuel cost, greenhouse gas emission penalty and payback period considering seasonal load variation. It is observed that PV+BESS is the most economical configuration. COE in standalone microgrid is higher than the conventional grid price. The results show that CO 2 emissions in hybrid PV+WT+DG+BESS are reduced by about 68% compared with the traditional isolated distribution system with DG.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Recently, modern power systems depend heavily on MicroGrids (MGs), which can accommodate Distributed Energy Resources (DERs) economically and with high flexibility. MGs integrated with DERs can assist in enhancing energy security, significant cost savings, and reduction in emission of greenhouse gases. In this paper, the assessment of operating performance of proposed MG system with DERs is employed to investigate the multi‐objective problems of cost optimization and economic scheduling. A grid‐connected Micro‐grid (MG) combined with solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), and Battery Energy Storage System (BESS) is implemented to model the problem. This proposed model is considered as a test system for cost optimization and battery charging/discharging optimization. The developed framework is presented as multi‐objective function with constraints that can be tackled using an effective optimization technique. The above stochastic multi‐objective problem is optimized using various commonly used Physics based Meta‐heuristic techniques such as Simulated Annealing (SA), Harmony Search (HS), Slime Mold Algorithm (SMA), Gravitational Search Algorithm (GSA), Black Hole Optimization (BHO), Sine Cosine Algorithm (SCA), Multiverse optimization (MVO) and Lightning Search Algorithm (LSA). The assessment of the aforementioned physics‐based optimization techniques used on the proposed MG test system is compared using the results. According to the analysis, Black Hole Optimization (BHO) and Lightning Search Algorithm (LSA) both provide greater cost savings overall and for battery charging, respectively. The suggested optimization methods will take the BESS charging/discharging pattern and total cost savings into account.

Islanded microgrids with hybrid energy sources are being adopted in remote areas to ensure reliability, energy security, cost-effective and reduce carbon emissions. This paper investigates the techno-economic benefits of islanded microgrid with hybrid energy sources. Optimal energy dispatch of hybrid system with photovoltaic (PV), wind turbine (WT), battery energy storage system (BES) and diesel generator (DG) is determined hourly subject to the minimization of net present cost (NPC). Various feasible case studies performed with hybrid energy sources and comparative analysis of techno-economic benefits are demonstrated for each case. Comparative analysis of techno-economic benefits is given with different configurations of PV + WT + DG + BES, PV + DG + BES and WT + DG + BES. A simulation study has been carried out using HOMER Pro optimization tool, and numerical results show that the hybrid microgrid system with PV + WT + DG + BES has minimum NPC and cost of energy compared to other configurations. Simulation results are obtained for the optimal capacity of PV, WT, DG, BES and SOC of the storage system.

This paper proposes a comprehensive strategy for the coordinated frequency control of a low-inertia islanded microgrid (MG). The main components of the MG include a double-fed induction generator (DFIG)-based wind turbine, a battery energy storage system (BESS), and diesel synchronous generators (DGs). In the proposed control framework, effective but simple coordination between the DFIG and the BESS is developed to handle the responsibility of fast-primary frequency support (PFS) and compensate for the deficiencies of DGs in providing the fast frequency response during sudden load disturbances. To minimize the consumption of BESS energy, the proposed coordinated control utilizes the maximum support capacity of the DFIG bound by the over-deceleration (OD) and overload (OL) constraints. The coordinated BESS prevents the OD and OL issues of DFIG during the support period, and compensates the shortages of the DFIG wind system for the certain frequency support under any wind condition. It also prevents the secondary frequency drop issue during the DFIG rotor speed recovery. To return to the normal condition after fulfilling the PFS functions, the DFIG and BESS support powers are brought back to zero via a secondary frequency control (SFC) applied in the DGs. In normal conditions, to optimally manage the MG energy efficiency, the BESS exchanged power is set to zero, and the DFIG is controlled in the maximum power point tracking (MPPT) operation through an efficient algorithm. A test system with a 33% load disturbance is modeled in a detailed time-domain simulation environment to evaluate and verify the effectiveness of the proposed design methodology. The simulation results show the superiority of the proposed coordinated control strategy compared with only DFIG or only BESS control under various wind conditions.

In this paper, two charging modes of battery energy storage system (BESS) for a stand-alone microgrid are analyzed. The stand-alone microgrid system consists of 50kW BESS, 50kW diesel generator (DG) and controllable loads, where BESS is composed of 115kWh battery bank and 50kW DC-AC inverter. The operation modes of the stand-alone microgrid system are divided into four modes, and BESS is connected to DG to charge an insufficient SOC (State of Charge) of battery bank through two charging modes. Charging mode I is that BESS performs constant voltage constant frequency (CVCF) control as main source and DG operates in active power control. On contrary, charging mode II is that DG performs CVCF control as main source and BESS is charged by constant current constant voltage (CC-CV) method. The operation of BESS is similar to grid-connected characteristic in the charging mode II, where BESS is charged from DG for increase of SOC. PR voltage + P current control in the stationary reference frame for charging mode I and PI + R control in the synchronous reference frame is applied for charging mode II. Stability of two charging modes is analyzed by using root locus in the discrete time z-domain. Demonstration site is constructed and performance of the proposed two control methods is verified through experiment, where THD of charging current is 1% in charging mode I and is 2.2% in charging mode II.

RETRACTED: Sustainability analysis of a hybrid renewable power system with battery storage for islands application