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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]

The evaluation of communities' vulnerability to prolonged power outages offers valuable insights for prioritizing improvements in infrastructure resilience, thereby alleviating societal consequences. This study proposes a data-driven approach aiming at developing a Social Vulnerability Index (SoVI) to prolonged power outages leveraging three county-level datasets in California including (1) demographic features, (2) power outage factors, and (3) backup power factors. Furthermore, the study conducts a sensitivity analysis on three distinct datasets under two scenarios (Scenario 1: the current SoVI in 2022, Scenario 2: the prediction of SoVI in the year 2030). The results of Scenario 1 indicate that the counties with more affected customers, the number of power outages, and less education attainment tend to be more vulnerable to power outages in 2022. Scenario 1 reveals that the number of affected customers and power outages are the primary features influencing around 29% and 18% of counties, while educational attainment, public Electric Vehicles (EVs) chargers, and homes with rooftop photovoltaic (PV) substantially impact approximately 32%, 11%, and 8% of counties, respectively. However, in Scenario 2, crucial factors affecting the anticipated SoVI in 2030 include public EV chargers, houses with rooftop PV, power outages, and adults living alone. In contrast to Scenario 1, the prevalence of adults living alone has emerged as a notable factor impacting SoVI in 2030, while both scenarios underscore the pivotal role of EV chargers in influencing SoVI concerning power outages. The proposed SoVI facilitates informed policy decisions and infrastructure improvements in energy resilience, resource allocation, and disaster preparedness, contributing valuable insights for targeted interventions in these domains. • A data-driven approach was proposed to quantify the social vulnerability index (SoVI) to power outages. • This paper applies Principal Component Analysis (PCA) to identify the most correlated features from county-level datasets. • The paper conducts a sensitivity analysis to predict SoVI in2030 and evaluate how counties' dynamics change over time. • This paper provides insights into how policymakers can reduce the social vulnerability to power outages in California. [ABSTRACT FROM AUTHOR]

Inequities in the built environment have long persisted in communities around the world. Prior studies have exposed that energy retrofit programs invest inequitably in residential buildings, leaving racial minorities with disproportionately less energy inefficient homes, regardless of income level. This inequity in energy performance and retrofit investments has yet to be studied for commercial and institutional buildings. Further research has shown that tree canopy coverage and vegetation is less dense in historically disadvantaged communities, contributing to a greater urban heat island effect and corresponding higher energy consumption for cooling compared to historically advantaged communities. Moreover, there is limited albeit emerging research that utilizes granular socioeconomic data with fine temporal scale energy consumption data to help identify energy inequalities among race and income groups. This paper examines the differential impact of energy efficiency retrofit installation on energy performance between advantaged vs. disadvantaged groups using data from U.S. public school buildings. Additionally, it studies how the effect of retrofits is compounded by the presence of vegetation surrounding the building. Utilizing hourly interval smart meter data from primary and secondary school buildings in California, the impact of retrofits are quantified using three energy metrics. Then the disparities in retrofit impact for advantaged vs. disadvantaged groups (as defined by three equity metrics) are evaluated using school-level demographic data. Lastly, regression on the percent change in monthly energy consumption from the pre-retrofit to post-retrofit period is used to quantify the incremental benefits of retrofits compounded by vegetation. Findings show that energy efficiency retrofits can improve energy performance and reduce energy inequities. Specifically, retrofits significantly increase energy efficiency and reduce the energy efficiency equity gap by up to 198%. Evidence shows that vegetation surrounding a building has a synergistic effect with mechanical retrofits for reducing energy consumption in respect to reduced cooling loads, even in cases where mechanical retrofits alone resulted in significant increases in energy consumption. Dense vegetation provides around 6% energy savings, similar in magnitude to savings from mechanical retrofit installation. This validates the effectiveness of retrofits for reducing energy consumption and improving energy equity, and demonstrates the ability of vegetation to provide additional energy savings beyond mechanical interventions. [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]

• Dispensed cost of hydrogen projected to reach cost parity by mid 2020s creating condition for self-sustainability. • Biomass-to-hydrogen assessed to be least-cost in the 2020s at roughly $2/kg but electrolytic ultimately becomes least cost. • Renewable hydrogen has the potential to be a cost-effective approach to decarbonization in multiple sectors. • Massive construction program needed to meet potential demand. • Significant government support needed to ensure sector scaling. This paper describes a multi-element analysis to assess least-cost approaches to scaling the renewable hydrogen sector to support deep decarbonization of applications requiring energy dense, transportable, and storable fuel. The analysis uses California as a reference market. The focus of the analysis is understanding the resource potential and cost evolution of renewable hydrogen dispensed as vehicle fuel (the anchor source of demand) and determining the feasibility and timing of achieving fuel cost parity with conventional fuels adjusting for fuel-economy. The analysis is novel in that is fully cross-sectoral on the demand side and multi-technology and spatially resolved at the facility level on the supply side. Production technology shares are assessed based on relative cost of produced hydrogen including feedstock constraints. Technology costs are benchmarked and projected using both secondary sources and primary analysis. The sectoral demand forecast is based on a combination of quantitative and qualitative assessment of cost competitiveness with alternative decarbonization strategies. The overall conclusion is that, with appropriate policy support, the RH2 sector can reach self-sustainability (price point at parity with conventional fuel on a fuel-economy adjusted basis) by the mid to late 2020s. The analysis also brings into focus the massive construction program needed to scale the renewable hydrogen sector at a regional scale. [ABSTRACT FROM AUTHOR]

• Propose a method for embedding demand flexibility into building benchmarking. • Utilizes emerging building characteristics and time-series energy data streams. • Apply method to case study of 306 buildings in Southern California, USA. • Demonstrates ability to cluster buildings by attributes of demand flexibility. • Can inform decarbonization performance-specific policy targeting for buildings. The intermittency of carbon-free renewables and the demand changes associated with the widespread push for electrifying the transportation and building sectors provides an opportunity for buildings to go beyond energy efficiency and push towards providing demand flexibility to the electricity grid. The duality of energy efficiency and demand flexibility is necessary for success in a sustainable and reliable energy transition. Current building energy benchmarking models are limited in their ability to integrate concepts of demand flexibility and/or utilize granular smart meter data. Thus, current benchmarking methods are focused annual energy usage and fail to incorporate how the time of use of energy consumption impacts emissions in a quickly changing energy grid. Without a more comprehensive view of energy usage and associated real-time emissions, current benchmarking methods are unlikely to realize the full decarbonization potential of buildings. New emerging data streams provide an opportunity to develop a new generation of benchmarking energy models that embed dimensions of energy efficiency, grid interactivity, and demand flexibility into their analysis. In this paper, we propose a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams. We first engineer features to produce a mix-type dataset that encompasses many attributes of grid-interactive and efficient buildings, and then we apply K-medoids using Gower's Distance to produce peer-group clusters. We apply the method to a case study of 306 primary and secondary schools in southern California, USA. The results show that the method effectively clusters buildings by attributes of demand flexibility and energy efficiency. The clustering results reveal patterns in inefficient building operations and demand inflexibility at the building peer group level. The interpretation of clusters can serve as an integrated energy efficiency and demand flexibility benchmarking model and inform performance-specific policy targeting for buildings that go beyond traditional efficiency measures. [ABSTRACT FROM AUTHOR]

• Zero net energy is feasible when clean fuel is combined with electrification. • Building energy consumption valuation method impacts the component sizing. • Time dependent valuation of energy in California reflects its cost and emissions. • Solid oxide fuel cell provides efficient electricity and heat at the building. • Building can go off-grid with optimized operation of battery and fuel cell. California is seeking to mandate Zero Net Energy (ZNE) buildings by 2030 as part of its efforts to combat climate change. A ZNE building is required to compensate its annual energy consumption from the grid (electricity or gas) by injecting clean electricity back to the grid. So far, the most common approach for a ZNE building is utilizing solar photovoltaic (PV) or a PV and battery energy storage system. One key problem is the feasibility of installing PV if the surface area requirement is too large to achieve ZNE; another issue is grid congestion during the off-peak hours of midday when a large number of houses export their excess solar power to the grid. Hence, other ZNE home scenarios need to be evaluated to tackle these restrictions in achieving California's climate goals. In this work, we proposed and evaluated different system arrangements to reduce the required PV installation that is commonly installed as power generation source for ZNE residential units in California. The aim of this technical analysis is to provide a societal level policy guidance to accelerate fulfilling the ZNE home mandate. Due to the availability of the gas grid and potential for having more renewable gas in the grid, we investigated using gas-based heat generation versus only electric-based heat appliances. In addition, we incorporated a residential solid oxide fuel cell (SOFC) on the building site as a micro combined heat and power (mCHP) to facilitate achieving ZNE. The impact and contributions of different components, including energy storage, on grid-dependence, and the overall energy demand are also studied. With analysis of different mix of technologies that could be used for a ZNE home, we propose the potential solutions and discuss their strengths and weakness. In this paper, energy performance and optimal PV capacity are assessed for a ZNE home in all 16 climate zones of California established by California Energy Commission (CEC). The ZNE measurements for all four scenarios are based on three metrics of energy evaluation: (1) site energy (energy measured by the meter used at the building site); (2) source energy (raw fuel consumption at the source of energy generation to account for energy loss in production and distribution); and (3) time dependent valuation (TDV) of energy, which has been developed by the CEC to comply with its building energy efficiency standards. The TDV of each energy type depends on the time of use, climate, grid constraints, and other factors that reflect its societal value at any given hour of the year. The PV size required by each ZNE metric is calculated and compared. The results proved that relying on some gas in addition to electricity improves the practicality of required PV for ZNE homes particularly in regions with high heating demands. It was found that depending on the energy metric (site, source, and TDV), a different arrangement is required for the most efficient ZNE performance. Unlike the site metric, using source and TDV metrics where electricity is more valuable than gas shows that having both gas and electricity significantly reduces the PV size. This encourages investment in introduction of more renewable gas in the grid as one possible solution to achieving ZNE in individual homes. The great impact of an mCHP SOFC on a ZNE home in terms of reliability and feasibility points to the need for research, development, and commercialization to reduce the cost of mCHP SOFCs in residential application. Also, the addition of a battery does not reduce the PV size with site and source metrics; it only reduces the electric grid reliance. However, with TDV, where the time of energy consumption affects its value, not only does it reduce grid reliance during peak hours, but also less PV is required to achieve ZNE. [ABSTRACT FROM AUTHOR]

One of the reasons for the emerging adoption of behind-the-meter distributed energy resources (DERs), particularly photovoltaic (PV) and storage, is the impressive decrease in technology costs over the last years together with favorable policy and regulatory environments that created early-stage incentives to the proliferation of these assets. However, when looking at the next decade, the evolution of the regulatory framework and the trajectory of technology costs are difficult to predict. This uncertainty poses a new challenge to prosumers and microgrid owners who are trying to find the best moment to invest in these DERs. In particular, unpredictable changes in the conditions of the investments may translate into economic risks to potential DER adopters, which raises the need for new risk-mitigation methods to support their investment decisions. To address this issue, this paper proposes a multi-period DER investment model with economic risk constraints, considering technology costs and regulatory uncertainties. A case study, involving a large building in California, is used to show that different types of economic risk constraints can affect not only the size, but also the optimal timing of these investments in a multi-year planning horizon with significant DER technology costs and regulatory uncertainties. • Adds technology and regulatory uncertainty to the DER planning. • Optimizes multi-year DER investments strategies with risk constraints. • Presents DER planning strategies in a "cost-risk" decision-support framework. • Analyzes how different risk criteria impact DER investment solutions. [ABSTRACT FROM AUTHOR]

To prepare for rapid growth in global electric vehicle adoption, grid and policy planners depend on detailed forecasts of future charging demand. In this paper we propose a novel holistic, scalable, probabilistic framework to produce large-scale estimates of electric vehicle charging load for long-term planning that capture real drivers' charging patterns. Our framework captures the uncertainty and stochasticity in charging demand by taking a graphical modeling approach. It has three core elements: driver groups, charging segment choices, and charging session time and energy requirements. The framework uses hierarchical clustering to group drivers by their charging histories, capturing their heterogeneous behaviors and preferences across different segments or types of charging. The framework uses probabilistic mixture models for each driver group's sessions to identify the unique charging behaviors observed within each segment. We illustrate its application with a large data set from California, profiling the charging patterns and unique driver clusters it identifies. Using the model knobs representing drivers' battery capacities, behavior, and segment access we present scenarios for California's charging demand in 2030 with 8 million passenger electric vehicles. Peak charging demand ranged from 3.3 to 8.7 GW across scenarios. Each was calculated in under 45 s on a laptop computer. • Novel data-driven, graphical model framework for large-scale electric vehicle charging. • Hierarchical clustering of electric vehicle drivers by charging behavior histories. • Driver clusters highlight patterns for mixed use of multiple charging segments. • Distributions over driver groups and behaviors determine future charging demand. • Scenarios presented for charging of 8 million electric vehicles in California in 2030. [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]

This paper presents an integrative methodology for managing and stabilizing the output of a wind/solar farm using storage devices in a cost effective and real-time manner. We consider the problem where a renewable farm should decide the amount of energy charged into, or withdrawn from, the battery given the stochastic and time-varying nature in the renewable energy power output. Our methodology features a seamless integration of a non-myopic decision framework and a sequential non-parametric predictive model based on functional principal component analysis. A key feature of our algorithm is that it quantifies costs over a rolling horizon where both predictions and decisions are updated on the fly as new data is acquired. Our technology is tested on the California ISO dataset. The case study provides a proof-of-concept that highlights both the benefits and ease of implementation of our forward looking framework. • We present a methodology for managing renewable farm outputs using storage devices. • A key feature of the algorithm is its lookahead decision framework where: costs are quantified over a rolling horizon; both predictions and decisions are updated on the fly as new data is acquired. • The algorithm is able to incorporate any optimization or forecasting approach given limitations on the time between real-time decision updates. • The presented approach is easily implementable in real-time settings where storage decisions may be updated regularly. [ABSTRACT FROM AUTHOR]