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Modern electrical power systems now require the spread of microgrids (MG), where they would be operating in either islanded mode or grid-connected mode. An inherent mismatch between loads and sources is introduced by changeable high renewable share in an islanded MG system with stochastic load demands. The system frequency is directly impacted by this mismatch, which can be alleviated by incorporating cutting-edge energy storage technologies and FACTS tools. The investigated islanded MG system components are wind farm, solar PV, Electric vehicles (EVs), loads, DSTATCOM, and diesel power generator. An aggregated EVs model is connected to the MG during uncertain periods of the generation of renewable energy (PV and wind) to support the performance of MGs. The ability to support ancillary services from the EVs is checked. DSTATCOM is used to provide voltage stability for the MG during congestion situations. The MG is studied in three scenarios: the first scenario MG without EVs and DSTATCOM, the second scenario MG without DSTATCOM, and the third scenario MG with all components. These scenarios are addressed to show the role of EVs and DSTATCOM, and the results in the third scenario are the best. The system voltage and frequency profile is the best in the last scenario and is entirely satisfactory and under the range of the IEEE standard. The obtained results show that both EVs and DSTATCOM are important units for improving the stability of modern power grids. The Matlab/Simulink program is considered for checking and validating the dynamic performance of the proposed configuration. [ABSTRACT FROM AUTHOR]

• Critical challenge in developing countries. • Limitations of demand flexibility. • Innovative multi-strategy approach with community engagement. • Quantified benefits and sensitivity analysis. Electricity shortage remains a critical challenge in developing countries striving to achieve the United Nations' universal electricity access goal by 2030. Limited fuel availability and limited funds for renewable energy investments, while demand flexibility is implemented to regulate electricity access, it faces limitations due to voluntary participation, rotating load-shedding schedules, and lack of electrification in certain communities. This research paper proposes a multi-strategy approach as an alternative solution by scheduling essential appliances and optimizing power flow in distribution grids. The article aims at community engagement and utility collaboration to improve electricity access. This paper proposes a multi-strategy framework for regulating social organization and utility operations, and attaining energy conservation, efficiency, and incentives. Optimization tool is developed to extend benefits of optimal scheduling of end user devices under power supply shortage, uncertainty of demand and grid operational codes, using multiple strategies regarding to consumers' fairness of electricity access in a community, and maximum electricity access per customer and region. Such strategies are also compared with the case of customized appliance priority of preference. The results demonstrate that energy efficiency and economic sustainability indices can achieve 94 % and 29.5 % improvement, respectively, compared to conventional methods. [ABSTRACT FROM AUTHOR]

This paper proposes a robust investment and operation model to attend the power and heat needs of a microgrid (MG) connected to the distribution system. The optimization algorithm decides on the best investment and operation of combined heat and power (CHP), boilers, PV power generation and battery energy storage systems (BESS). For the BESS, the algorithm estimates the optimal energy storage capacity (MWh) as well as the maximum hourly delivery capacity (MW). The non‐linear and non‐concave heat rate chart is recast by a mix‐integer linear model to have a tractable and precise model. The model considers the uncertain in some parameters using probability density function (pdf) to portrait its behavior. Thus, the problem has been modeled using stochastic programming approach, and its objective function is the expected value of the annual operational cost. The model is tested using a real case where two adjacent consumers share the power and heat facilities to minimize the overall up to 17% depending on the gas price scenario. The results demonstrate the benefits of employing different technologies and the synergies of all technologies operating together. [ABSTRACT FROM AUTHOR]

Deferrable power-driven demands such as water and thermal ones possess a capability of energy storage which can be exploited to further optimize electric systems. This paper proposes a new optimized scheduling framework for electric grids by simultaneously exploiting the high-inertia thermal dynamics and lossless water storage. Unlike previous ideas in the literature, both water and thermal demands are managed to realize the long-term and short-term load-shifting strategies, respectively. First, distributed water/thermal loads are aggregated where the virtual storage can be represented by battery models. Then, a further optimized scheduling framework is proposed with a mixed-integer nonlinear programing problem. Comparative studies show that the water storage offers outstanding flexibility for electric system via pumps scheduling, especially for the electric load-shifting strategy in a long time-horizon thanks to the lossless water-storage process. Meanwhile, the thermal storage can directly support short-term electric load-shifting to avoid price spikes of electricity. Numerical results show that the proposed method can reduce the total cost of micro-grids by maximizing the usage of renewable energy sources, avoiding price spikes, and reducing dependence on high-cost centralized energy-storage facilities provided that the critical water-energy demands are preserved. [ABSTRACT FROM AUTHOR]

This paper investigates an optimal sizing strategy for an islanded building microgrid. The microgrid composites a rooftop Photovoltaic (PV) system, a Battery Energy Storage System (BESS), an ice-Thermal Energy Storage System (ice-TESS), and loads. The loads are divided into two sets based on their ability to participate in demand response: i) Plugged Loads (PL) such as lights, and ii) Cooling Loads (CL) such as air-conditioners. The microgrid is islanded and loads must be supplied with local generation resources. Therefore, the BESS is deployed to offset the PV output's variability, and the absence of PV power supply at night time. However, relying only on the BESS incurs high stress and shortens the BESS's lifetime. Hence, we propose an optimal sizing strategy of the microgrid constituents, where the BESS coordinates with the ice-TESS to maintain the balance between generation and load in the microgrid. Nevertheless, the dispatch commands cannot swing freely between the two ESSs because of the difference in the type of energy delivery. Specifically, the BESS stores electric energy and can supply both PL and CL. On the other hand, the TESS can only supply the CL. Hence, the BESS-TESS coordination is also aided by a customer-friendly shifting and curtailment mechanism of CL. The design incorporates the effect of weather uncertainty on the PV output. Weather variations are imitated using Recurrent Neural Networks trained on 19-years of contiguous hourly weather data. After optimizing the sizes of the microgrid constituents, the optimal sizes are used in a detailed dynamic model of the system for a real-time simulation on the OPAL-RT platform. The validation results demonstrate the successful coordinated operation of the microgrid constituents which are cost-effective in sizing. [ABSTRACT FROM AUTHOR]

Inverter-based distributed generators (DGs) have been customarily used for voltage unbalance (VU) mitigation in microgrids. The sole dependency on DGs for VU mitigation may not be justified, particularly in islanded microgrids. Demand side management can be a potential candidate for VU mitigation in microgrids. Therefore, this paper presents a new VU mitigation scheme for an islanded microgrid by coordinating photovoltaic (PV) grid-tied inverters, and thermostatically controlled loads (TCLs). A negative sequence compensation loop working in parallel with a positive sequence compensation loop is designed for PV inverters for VU mitigation. Also, a voltage dependent model of TCLs, unlike the conventional one, along with the control strategy for VU mitigation is presented. The proposed control of TCLs not only minimizes VU, and hence, increases the dynamic reactive power reserve of the inverter based DGs, but also effectively maintains the customers’ thermal comfort. The proposed VU mitigation scheme is studied with comprehensive simulations in PSCAD/EMTDC, and is verified using a real-time digital simulator, OPAL-RT. Simulations and real-time results of the proposed scheme demonstrated the regulation of VU factor within the permissible range defined by IEC and CIGRE Working Group 36.07. [ABSTRACT FROM AUTHOR]

Conventional droop characteristics for microgrids have been primarily proposed to achieve proper load sharing between the various distributed generation units. This paper proposes a different perspective for designing autonomous microgrids, which aims at reducing the power consumption of the microgrid by utilizing the concept of conservation voltage reduction. The proposed control strategy relies on a voltage–current (V–I ) droop characteristic, designed to perform demand side management through voltage control. The effectiveness of the proposed control scheme is demonstrated through time-domain simulation studies considering voltage dependent loads tested over a range of loading conditions and power factors. The performance of the proposed scheme is compared to the conventional and adaptive P-f/Q-V decentralized droop control. Further, a comparison of the transient analysis of the three droop schemes in case of a fault has been evaluated. The proposed V–I droop control scheme can maximize the number of loads being supplied during power shortage. [ABSTRACT FROM PUBLISHER]

Cloud energy storage systems (CES) are a new paradigm for the application of consumer-side energy storage in residential community microgrids. By transforming traditional consumers into self-sustaining and utility consumers, CES facilitates interaction between consumers and utilities as well as between consumers. Residential CES development is complicated by the flexible capacity of CES batteries and the uncertainty of supply. This paper recommends the CES system for residential prosumers. A machine learning-based uncertainty quantization and an artificial ecosystem optimization (AEO) method are used to determine optimal battery capacity considering PV, load, and price uncertainty. Compare this algorithm to the other four optimization algorithms to find out how well it works. The feasibility and profitability of deploying CES with residential PV are assessed. This problem minimizes the user's cost and maximizes the profit of the CES operator. The sensitivity analysis of CES is analyzed for various storage capacity penetrations. Further, optimal battery capacity determines the use of an AEO algorithm. Based on a PJM dataset of residential PV, load, and electricity price, simulation results demonstrate that the suggested framework integrated with a distributed PV system is more economical to DES. • Modeling of cloud energy storage. • Artificial intelligence based learning uncertainty quantification mechanism. • Proposed an optimization algorithm for optimal battery capacity for CES. • Dynamic price mechanism used to obtain optimal charging and discharging profile for better the utilization of CES. [ABSTRACT FROM AUTHOR]