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Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is evaluated through a proposed method of levelized cost of energy (LCOE) for clustered generation portfolios. Effective renewable economics is applied to assess resource adequacy, annual carbon emissions, renewable capacity factor, over generation, and cost to build between thermal dispatch scenarios with incremental increases in VRE penetration. Solar PV and wind generation temporally complement one another in the region studied, and the combination of the two is beneficial to renewable energy integration. Furthermore, replacing older coal units with cleaner and agile natural gas units increases renewable hosting capacity and provides further pathways to decarbonization. Minute-based chronological simulations enable the assessment of renewable effectiveness related to weather-related variability and of complementary technologies, including energy storage for which a sizing procedure is proposed. The generally applicable methods are regionally exemplified for Kentucky, USA, including eight scenarios with four major year-long simulated case studies and 176 subcases using high performance computing (HPC) systems. [ABSTRACT FROM AUTHOR]

The forecast and estimation of total electric power demand of a residential community, its baseload, and its heating ventilation and air-conditioning (HVAC) power component, which represents a very large portion of a community electricity usage, are important enablers for optimal energy controls and utility planning. This paper proposes a method that employs machine learning in a multi-step integrated approach. An LSTM model for total electric power at the main circuit feeder is trained using historic multi-year hourly data, outdoor temperature, and solar irradiance. New key temperature indicators, TmHAVC, corresponding to the standby zero-power operation for HVAC systems for summer cooling and winter heating are introduced using a V-shaped hourly total load curve. The trained LTSM model is additionally run with TmHVAC and zero irradiance inputs yielding an estimated baseload, which is representative of typical occupancy patterns. The HVAC power component is disaggregated as the difference between total and baseload power. Total power forecasts of an aggregated residential community as seen by major distribution lines are experimentally validated with a satisfactory MAPE error below 10% based on a 4-year dataset from a representative suburban community with more than 1800 homes in Kentucky, U.S. Discussions regarding the validity of the separation method based on combined considerations of fundamental physics, statistics, and human behavior are also included. [ABSTRACT FROM AUTHOR]

Net zero energy (NZE) houses purchase zero net metered electricity from the grid over a year. Technical challenges brought forth by NZE homes are related to the intermittent nature of solar generation, and are due to the fact that peak solar generation and load are not coincident. This leads to a large rate of change of load, and in case of high PV penetration communities, often requires the installation of gas power plants to service this variability. This article proposes a hybrid energy storage system including batteries and a variable power electric water heater which enables the NZE homes to behave like dispatchable generators or loads, thereby reducing the rate of change of the net power flow from the house. A co-simulation framework, INSPIRE+D, which enables the dynamic simulation of electricity usage in a community of NZE homes, and their connection to the grid is enabled. The calculated instantaneous electricity usage is validated through experimental data from a field demonstrator in southern Kentucky. It is demonstrated that when the operation of the proposed hybrid energy storage system is coordinated with solar PV generation, the required size and ratings of the battery would be substantially reduced while still maintaining the same functionality. Methodologies for sizing the battery and solar panels are developed. [ABSTRACT FROM AUTHOR]

Typically, solar inverters curtail or “clip” the available power from the photovoltaic (PV) system when it exceeds the maximum ac capacity. This article discusses a battery system connected to the dc link of an inverter to recuperate this PV energy. Contrary to conventional approaches, which employ two dc–dc converters, one each for the battery and solar PV system, the proposed configuration utilizes a single dc–dc converter capable of simultaneously operating as a charge controller and a maximum power point tracking (MPPT) device. In addition to improving the overall system capacity factor, increasing the conversion efficiencies, and ensuring MPPT stability, the proposed configuration offers a simple solution for adding energy storage to existing PV installations. With this configuration, the excess power that will otherwise be curtailed due to inverter rating limitations is stored in the battery and supplied to the grid during periods of reduced irradiance. Moreover, a systematic methodology for sizing a dc-bus connected battery to minimize total PV energy curtailed was developed using an annual PV generation profile at the Louisville Gas and Electric and Kentucky Utilities E. W. Brown solar facility at Kentucky. The detailed behavior of the proposed system and its power electronics controls and operations were validated with case studies developed in PSCAD/EMTDC software for variable power generation and PV output power smoothing. [ABSTRACT FROM AUTHOR]