Malcolm McCulloch

Future smart grid virtual power plants (VPPs) are considered for development based on industry communication standards for electric vehicle (EV) chargers such as Open Charge Point Protocol (OCPP), IEC 15118, and IEC 61851. To support research and development of computationally intelligent controls for distributed EV batteries, a python-based API OpenDSS VPP framework is utilized with thousands of experimental smart meter profiles, the IEEE 123 node test feeder, and hundreds of national survey-based EV modules for conventional and optimal charging and vehicle-to-grid (V2G) control development to mitigate any voltage violations and reduce peak load. A methodology is proposed for model-predictive control (MPC) optimization considering power flow calculations for physical network impacts as part of the objective function for each candidate design. It is simulated in a first approach to minimize utility cost and obtain a near-constant substation load. Within the combined experimental and synthetic neighborhood, natural disaster shark-curve type charging events and typical patterns are evaluated through the newly proposed terms of “EV hourly and daily homogeneity” to find adverse effects starting at 20% and 50%, respectively with a sensitivity study showing up to 1.45MWh increase of system losses during maximum overlap. Reductions up to 26% in cost during the VPP time window are found given assumptions for CA, USA retail rate and EV owner compensation. In the second formulation, a multi-objective problem to minimize both system losses and the substation load to a near constant with 5% tolerance and 45% reduction in system losses, i.e. 135 and 40kW respectively, shows the power saving benefits of VPP operation beyond cost reductions.

Low voltage dc microgrids are an increasingly viable option for low-volume distribution of electrical power in developing countries. 48V systems have proven popular, owing largely to familiarity from the automotive sector, but there remains to date no universal consensus on optimal system voltage. In this work we propose the use of ±60V bipolar dc distribution and explore the relative advantages and disadvantages of such an approach from the standpoint of electrical safety. Despite significant technical advantages over existing ac and unipolar 48V dc microgrids, two safety aspects must be considered: the detection and extinguishing of series arcs and control of touch safety voltages. We show that an arc suppression technique using load-side voltage monitoring can reliably detect and extinguish series arcs. This functionality has been incorporated into a practical system providing protection against series, line-line and line-earth arc faults. A comprehensive touch voltage assessment with reference to IEC 60479 confirms that the proposed system voltage does not present risk of ventricular fibrillation based on values of body impedance corresponding to the 5th percentile of the population.

Large ensembles of global temperature are provided for three climate scenarios: historical (2006–16), 1.5 °C and 2.0 °C above pre-industrial levels. Each scenario has 700 members (70 simulations per year for ten years) of 6-hourly mean temperatures at a resolution of 0.833° ´ 0.556° (longitude ´ latitude) over the land surface. The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository as NetCDF V4 files.

Toward the planning and development of future electric power systems with extremely large penetration of renewable resources (DERs), detailed assessment of power and energy potential is needed considering both spatial and temporal data per region. Within this paper, a methodology for spatio-temporal DER capacity potential considering land cover types and weather variation are presented using spatio-temporal data. Additionally, an application of empirical orthogonal functions (EOFs) and max-p unsupervised learning techniques is proposed for DER generation to identify zones of similar output power in space and time. A detailed case study for the example region of Kentucky, USA is completed with state-of-the-art utility scale solar photovoltaic (PV) panels, wind turbines, and publicly available data from the National Aeronautics and Space Administration (NASA) Earthdata resource and the National Land Cover Database (NLCD). Annual estimates of wind and solar PV power for the example region are found to meet the state's public annual energy demand, even in the low land usage case. Further efforts to decarbonize energy generation and build additional renewable energy capacity are supported through the methodology and case study.

Extreme events, exacerbated by climate change, pose significant risks to the energy system and its consumers. However there are natural limits to the degree of protection that can be delivered from a centralised market architecture. Distributed energy resources provide resilience to the energy system, but their value remains inadequately recognized by regulatory frameworks. We propose an insurance framework to align residual outage risk exposure with locational incentives for distributed investment. We demonstrate that leveraging this framework in large-scale electricity systems could improve consumer welfare outcomes in the face of growing risks from extreme events via investment in distributed energy.

Urban heat islands are compounding the dangerous impacts of urban heat exposure globally. Cities across geographies require immediate and advanced urban climate information with enough resolution to detect risks and prioritise local climate adaptation interventions for their people. This research analyses the applicability of 550,000 citizen weather stations globally, a network 12 times larger than professionally operated weather stations, to measure urban climate at the highest spatio-temporal resolution and support climate adaptation policies globally. Cities with higher citizen data availability worldwide are identified, and a first open-source procedure in Python is proposed that cities around the world can use. The approach is validated in London, integrating temperature data from Netatmo and Wunderground platforms. The validation tests showed how pre-processing techniques improve citizen data accuracy, decreasing mean temperature deviation from 0.98ºC to 0.48ºC, inside the precision range of sensors. Data analytic methods identified significant implications of urban overheating in the case of London, increasing annual cooling degree hours by 60% in some urban areas, with different night and daytime hot spots. Aerodynamics and imperviousness affected urban overheating at night and daytime differently, requiring different localised interventions. Such large-scale application and scalability of open-access and readily available citizen data networks can efficiently support and monitor localised climate adaptation to mitigate the impacts of rising heat.

In alignment with the Paris Agreement, the city of Oxford in the UK aims to become carbon neutral by 2040. Renewable energy help achieve this target by reducing the reliance on carbon-intensive grid electricity. This research seeks to optimally size solar photovoltaic and lithium battery storage systems, reducing Oxford's grid electricity reliance in buildings. The analysis starts with modeling the electricity demand. The model uses Elexon electricity settlement profiles, and assembles them into the demand profile according to the quantity and types of buildings in Oxford. Then, solar generation is modeled using Pfenninger and Staffell's method. Solar photovoltaic and lithium storage systems are sized using a hybridized analytical and iterative method. First, the method calculates the solar system size search range, then iterates through the range. At each solar size, the method calculates and iterates through the storage system size search range. Within each iteration, the renewable system is simulated using demand and generation data with a simplified system set-up and the conventional operation strategy. The method outputs combinations of solar system capacity, storage system capacity, and grid electricity import. Each combination's levelized cost of electricity is calculated, and the lowest cost combination is the optimal sizing. Solar and storage system costs are projected from 2019 to 2100, and the optimal sizing is calculated for each year. The result shows that solar photovoltaic is economically competitive, but lithium storage cost is still too high. As solar and storage prices continue to drop, they will take up greater portions of the energy system. However, there will always be a need for the grid, as it provides flexibility and can meet demands that are too costly for solar and storage

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.