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• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents the first systematic study on power control strategies for Modular-Gravity Energy Storage (M-GES), a novel, high-performance, large-scale energy storage technology with significant research and application potential. Addressing the current research gap in M-GES power control technology, we propose two corresponding compensation modes and several power control strategies for Hybrid M-GES and M-GES. The effectiveness of these strategies is validated through simulations on the MATLAB/Simulink platform, including tests using sinusoidal power and feasibility verification based on a natural California load curve. Additionally, we analyze the normal and abnormal operating conditions of M-GES. In conclusion, the characteristics of the proposed control strategies are summarized, providing a guideline for further research. [Display omitted] • Modular-gravity energy storage (M-GES) power control system studied. • Two compensation modes and four control strategies are systematically studied and validated. • The center switching method with hysteresis control (CSM-H) excels in continuous compensation mode. • The center switching method with hysteresis control and dead zone (CSM-HD) excels in overlap compensation mode. • Based on the power control system proposed in this paper, the M-GES plant performs highly in natural power systems. [ABSTRACT FROM AUTHOR]

With many countries planning to significantly increase grid renewable energy penetration levels, we consider the role of wave energy in supply–demand matching. We investigate how incorporating wave power into an offshore wind farm affects farm power predictability, smoothness, required energy storage capacity, and cost. In this paper, we do a first-order cost analysis of an offshore farm comprised of floating wind turbines and wave energy converters that are both standalone and combined and onshore compressed air energy storage. Then, we do a parameter sweep investigation of an isolated power network supplied by varied grid renewable energy penetration levels supplemented by natural gas, varied distribution of renewable energy between wind and wave power, and varied power capacity of a compressed air energy storage system supplying power to a shoreline community. For each parameter set, we consider the historical hourly electricity demand and wind-sea data of a coastal California community over a year, and optimize the energy storage schedule to reduce curtailed power, stored energy, and base gas plant operational cost. We show that a co-located wind-wave farm has smoother power supply, less energy curtailment, and higher farm-to-grid efficiency than a solely wind farm. That is, a 50%–50% wind-wave farm has a 15% smaller coefficient of variation in the power supply, 6% less curtailed power, and 2% higher farm-grid efficiency than a 100% wind farm when the grid is 100% renewable energy. These benefits of wave power potentially decrease the need for interconnecting regional transmission lines to match power supply with demand. The intent of this paper is to provide baseline system technical results to help future researchers and policy makers make decisions about offshore hybrid wind-wave-storage farms. • We do a joint parameter sweep and energy storage schedule optimization on the farm. • A co-located wind-wave farm has 15% smoother power than a wind farm. • A co-located wind-wave farm has 6% less energy curtailment than a wind farm. • A co-located wind-wave farm has 2% higher power efficiency than a wind farm. • Attaching a wave energy converter (WEC) to a wind turbine reduces WEC cost by 43%. [ABSTRACT FROM AUTHOR]

• Renewable hydrogen can compete with lithium-ion batteries for seasonal storage. • Hydrogen's competitiveness depends on heat rate of plant it is replacing. • Net present value of hydrogen-fired gas turbine varies based on operation of plant. • Lack of infrastructure will impede demand growth for hydrogen in the United States. Non-emitting variable renewable energy (VRE) resources are needed on the power grid if the United States is to "deeply decarbonize" the power sector. The intermittent nature of these resources makes them difficult to integrate into the power system. Existing energy storage technologies, such as lithium-ion (LI) batteries, could be used to aid the integration of these resources, but these technologies are sized to produce power for hours at a time before needing to be charged again. While these energy storage technologies could address daily imbalances between supply and demand for electric power, they cannot address the seasonal nature of power production of VREs. This paper details a methodology to estimate the levelized cost of energy (LCOE) of meeting this seasonal imbalance with either a hydrogen-fired gas turbine (HFGT) or lithium-ion battery system (LI) as a measure of economic efficiency of the technologies. Applying our model, we find the average LCOE associated with meeting this seasonal imbalance is $2400/MWh using a HFGT fueled with green hydrogen and $3000/MWh using a LI. If we allow the model to operate the HFGT with blue hydrogen the average LCOE decreases to $1560/MWh. However, we find the power prices required to justify investment in an HFGT to replace a natural gas-fired gas turbine are considerably higher than those seen in the market today. [ABSTRACT FROM AUTHOR]

California has set two ambitious targets aimed at achieving a high level of decarbonization in the coming decades, namely (i) to generate 60% and 100% of its electricity using renewable energy (RE) technologies, respectively, by 2030 and by 2045, and (ii) introducing at least 5 million zero emission vehicles (ZEVs) by 2030, as a first step towards all new vehicles being ZEVs by 2035. In addition, in California, photovoltaics (PVs) coupled with lithium-ion battery (LIB) storage and battery electric vehicles (BEVs) are, respectively, the most promising candidates for new RE installations and new ZEVs, respectively. However, concerns have been voiced about how meeting both targets at the same time could potentially negatively affect the electricity grid's stability, and hence also its overall energy and carbon performance. This paper addresses those concerns by presenting a thorough life-cycle carbon emission and energy analysis based on an original grid balancing model that uses a combination of historical hourly dispatch and demand data and future projections of hourly demand for BEV charging. Five different scenarios are assessed, and the results unequivocally indicate that a future 80% RE grid mix in California is not only able to cope with the increased demand caused by BEVs, but it can do so with low carbon emissions (<110 g CO2-eq/kWh) and satisfactory net energy returns (EROIPE-eq = 12–16). [ABSTRACT FROM AUTHOR]