On-site Photovoltaic Generation Research Articles

Large-scale EV charging scheduling considering on-site PV generation by combining an aggregated model and sorting-based methods

Large-scale electric vehicle (EV) charging scheduling is highly relevant for the growing number of EVs, while it can be complex to solve. A few existing studies have applied a two-stage scheduling approach to reduce computation time. The first stage approximates the optimal overall load, and the second prioritizes the charging. This work also attempts to apply such an approach for large-scale EV charging considering on-site photovoltaic (PV) generation at a workplace. However, validation and analysis are missing to address whether and why the two-stage approach is suitable. Besides, the existing studies lack exploring different methods to prioritize charging. This work investigates the two-stage approach. Simulation results show the non-uniqueness of the optimal solution from the optimal individual model, and guided by the optimal overall load, sorting-based methods can often lead to an optimal solution, while non-optimal solutions only cause decreases in the load-matching performance with a median value of less than 1%. The aggregated model usually cannot achieve the optimal overall load due to model simplifications. However, further applying sorting-based methods will reduce the differences between the final and the optimal overall load. Thus, the two-stage approach is suitable for this study, and further simulations show that it can achieve almost the optimal annual performance with around 1/57 of the computation time. Furthermore, this study explores different methods to prioritize charging. Simulation results show no difference in performance, while the Least Laxity First method leads to around 54.6% more switching.

An analytical method for identifying synergies between behind-the-meter battery and thermal energy storage

Electric utilities build generation capacity to meet the highest demand period, and they often pass on the costs associated with these peaking generators to building owners through demand charges. Building owners can minimize these demand charges by shifting energy use away from peak periods with behind-the-meter storage. This storage can include batteries, which can directly shift the metered load, or thermal energy storage, which can shift thermal-driven electric loads like air conditioning. However, there is a lack of research on how best to combine battery and thermal energy storage. In this study, we develop an analytical sizing method to calculate the potential demand reduction and annualized cost savings for different combinations of thermal and battery energy storage sizes. We show that adding batteries to a thermal energy storage system can increase the total system's load shaving potential. This is particularly true when the building has onsite photovoltaic generation or electric vehicle charging, which add significant variability to the load shape. We also show that for a given total storage size, selecting a higher fraction of thermal energy storage can significantly lower the cycling of the battery, and therefore extend the battery life. This, combined with the expected lower first cost of thermal energy storage materials compared to batteries, shows that hybrid energy storage systems can outperform a standalone battery or standalone thermal storage system. Assuming the thermal storage has a capital cost 6x lower than the battery, our analysis shows that the optimal system is 71% thermal energy storage and 29% battery energy storage for a scenario with electric vehicle charging. The annualized cost savings for this system are $48.6 k/yr, whereas an equivalently sized standalone thermal energy storage system would provide annualized cost savings of $28.5 k/yr and a standalone battery would lead to savings of $8.72 k/yr. The hybrid system also reduces battery cycling by 52% compared to a standalone battery, extending battery lifetime.

Hierarchical Operation Management of Electric Vehicles for Depots With PV On-Site Generation

Solar photovoltaic (PV) generation is a promising energy cost-saving selection for depots that use electric vehicles (EVs) to deliver goods; however, the intermittency of power output renders it challenging to realize reliable and economical operations. This paper proposes a two-stage hierarchical operation management method of EVs for depots with on-site PV generation. In the day-ahead stage, considering real road networks and battery swapping mode, a coordinated scheduling model of delivery service and charging/discharging for EVs is constructed to minimize the overall energy cost and logistics delivery cost by using the forecasted PV power output. Moreover, an effective solution approach based on the natural aggregation algorithm and Floyd algorithm is developed for the model. In the actual operation stage, a model prediction control (MPC)-based rolling horizon operation strategy is proposed to correct EVs’ charging/discharging decisions with the realization of real-time PV power output, aiming at to minimize the deviation in the actual and day-ahead scheduled interactive power between the depot and upper grid. Finally, simulations are conducted to validate the proposed methods.

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

Operational performance and grid-support assessment of distributed flexibility practices among residential prosumers under high PV penetration

This study explores the impacts of increased photovoltaic (PV) penetration on a real-world power grid. Detailed operational scenarios of existing distributed energy technologies, including grid-tied PV, heat pump, micro-cogeneration, and battery systems among smart residential prosumers are examined using smart meter data. Then, to quantify the grid-support interactions of the decentralized electricity generation and consumption under high grid PV penetration, we introduce grid-support indicators to recognize their induced month-to-month variations based on the dynamic grid spot trading price and carbon emission density profiles. The results show that increased electricity consumption is attractive when the flexible consumption is shifted to a low grid spot trading price and carbon emission intensity period. The grid-support benefits of distributed generators are improved when their output enables a reduction in imported electricity during the grid residual load peak period. The direct simple integration of distributed PV generation makes it difficult to achieve grid-support operation at high solar penetration levels. The analysis results provide insight into the favorable scheduling of the heat pump consumption for time periods corresponding to a large quantity of available on-site PV generation to improve the grid-support performance. The results would help policymakers govern grid-support assets more equitably from a system-level perspective.

Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability

In the last few decades, there has been a strong trend towards integrating renewable-based distributed generation systems into the power grid, and advanced management strategies have been developed in order to provide a reliable, resilient, economic, and sustainable operation. Moreover, demand response (DR) programs, by taking the advantage of flexible loads’ energy reduction capabilities, have presented as a promising solution considering reliability issues. Therefore, the impacts of combined system architecture with on-site photovoltaic (PV) generation units and residential demand reduction strategies were taken into consideration on distribution system reliability indices in this study. The load model of this study was created by using load data of the distribution feeder provided by Bosphorus Electric Distribution Corporation (BEDAS). Additionally, the reliability parameters of the feeder components were determined based on these provided data. The calculated load point and feeder side indicators were analyzed comprehensively from technical and economic perspectives. In order to validate the effectiveness of the proposed structure, four case studies were carried out in both DigSILENT PowerFactory and MATLAB environments.

Influence of Occupant Behaviour on the State of Charge of a Storage Battery in a nearly-Zero Energy Building

The development of nearly zero energy buildings (nZEB) is a goal being set for many countries for the next years. In order to achieve that goal, on-site energy generation is needed, and the use of photovoltaic and storage systems is a possible strategy. Hence, the objective of this work is to evaluate the impact that occupant behaviour has on the batteries energy storage systems (BESS) of an nZEB. This study considers as a case study an nZEB with lightweight construction, located in the university campus, and used by different occupants. The case study has 12 PVs located on the root and a second-life BESS from an electrical vehicle (EV). The monitoring system consists of interior T, RH, CO2 and energy sensors and an exterior on-site meteorological station. The research started with a state of the art review of the support policies for the on-site photovoltaic generation and energy storage. Different storage charge profiles were obtained as different occupant behaviours were applied to the test building. In total, it were considered three occupant profiles and a base scenario was the unoccupied. The occupant behaviour showed to be determinant in the state of charge of the BESS independently from the impacts in the indoor temperature. The need to know the profile of the occupants before the design of the buildings was enhanced.

Attractiveness of Using Photovoltaic Panels in a Building Connected to a Mainly Renewable Electricity Grid

Photovoltaic (PV) panels contribute to overall building’s loads, but generally have their impacts offset at the operational stage. For increasingly renewable electricity grids, PV’s contribution to lowering non-renewable energy becomes less significant. This paper aims at investigating the non-renewable cumulative energy demand (CEDnren) and global warming potential (GWP) payback times associated to onsite PV generation in the highly renewable Brazilian grid, considering a 50-year building service life. Operational energy consumption was simulated in Energy Plus. CEDnren and GWP were calculated through the CED method and CML-IA, respectively. SimaPro 7.3 and Ecoinvent 2.2 supported performed LCAs. Different PV settings were analyzed to rank the most effective technological options. Amorphous and single-Si panels performed worst (around 17 years of non-renewable CED payback time, whilst for GWP the payback time was much shorter for all technologies). PV’s production and replacement loads played a significant role, therefore technological investments to increase panels’ durability and improve manufacturing efficiency could ensure its attractiveness.

Potential Impacts of Net-Zero Energy Buildings With Distributed Photovoltaic Power Generation on the U.S. Electrical Grid

This study evaluates the potential aggregate effects of net-zero energy building (NZEB) implementations on the electrical grid in a simulation-based analysis. To estimate the impact of NZEBs on the electrical grid, a simulation-based study of an office building with a grid-tied photovoltaic (PV) power generation system is conducted. This study assumes that net-metering is available for NZEBs such that the excess on-site PV generation can be fed to the electrical grid. The impact of electrical energy storage (EES) within NZEBs on the electrical grid is also considered in this study. Different levels of NZEB adoption are examined: 20%, 50%, and 100% of the U.S. office building stock. Results indicate that significant penetration of NZEBs could potentially affect the current U.S. electricity demand profiles by reducing purchased electricity from the electrical grid and by increasing exported electricity to the electrical grid during peak hours. Annual electricity consumption of simulated office NZEBs in the U.S. climate locations is in the range of around 94–132 kWh/m2 yr. Comparison of hourly electricity demand profiles for the actual U.S. demand versus the calculated net-demand on a national scales indicates that the peak percentage difference of the U.S. net-electricity demand includes about 10.7%, 15.2%, and 9.3% for 100% of the U.S. NZEB stock on representative summer, transition, and winter days, respectively. Using EES within NZEBs, the peak percentage differences are reduced and shifted to the afternoon, including 8.6%, 13.3%, and 6.3% for 100% of the U.S. NZEB stock on each representative day.

Matching of Local Load with On-Site PV Production in a Grid-Connected Residential Building

Efficient utilization of renewable generation inside microgrids remains challenging. In most existing studies, the goal is to optimize the energy cost of microgrids by working in synergy with the main grid. This work aimed at maximizing the self-consumption of on-site photovoltaic (PV) generation using an electrical storage, as well as demand response solutions, in a building that was also capable of interacting with the main grid. Ten-minute resolution data were used to capture the temporal behavior of the weather. Extensive mathematical models were employed to estimate the demand for hot-water consumption, space cooling, and heating loads. The proposed framework is cast as mixed-integer linear programming model while minimizing the interaction with the grid. To evaluate the effectiveness of the proposed framework, it was applied to a typical Finnish household. Matching indices were used to evaluate the degree of overlap between generation and demand under different PV penetrations and storage capacities. Despite negative correlation of PV generation with Finnish seasonal consumption, a significant portion of demand can be satisfied solely with on-site PV generation during the spring and summer seasons.

Cost-benefit analysis of nZEB energy efficiency strategies with on-site photovoltaic generation

Many studies on the deployment of and investment in renewable energy (RE) technologies have focused on job creation associated with energy production at the macroeconomic level and across renewable energy technologies. We propose another perspective, the use of solar photovoltaic (PV) technology to attain a nearly zero-energy building (nZEB) class. The aim of this research is to investigate the costs and benefits for private and public entities when constructing nZEB or adopting nZEB policies. A quantitative research approach is taken when modelling required PV capacities, net present cash flows, subsidies, and job generation. Findings show that at current electricity tariffs and solar PV system capacities and production levels, single family houses, apartment buildings, and other building types require 0.044 €/kWh, 0.037 €/kWh, and 0.024 €/kWh, respectively, in government subsidies on energy sold back to the grid. Office buildings were profitable without the subsidy. In this study, we argue that investments in RE, specifically, PV technology, will bring in approximately 2.1 M€ of additional revenue to the Estonian government over a 20 years period as tax return overruns subsidies. However, nZEB investments are expected to become cost-optimal without subsidies, due to the increasing efficiency and decreasing costs of PV systems.

Dynamic Control and Optimization of Distributed Energy Resources in a Microgrid

As we transition towards a power grid that is increasingly based on renewable resources like solar and wind, the intelligent control of distributed energy resources (DER) including photovoltaic (PV) arrays, controllable loads, energy storage and plug-in electric vehicles (EVs) will be critical to realizing a power grid that can handle both the variability and unpredictability of renewable energy sources as well as increasing system complexity. Realizing such a decentralized and dynamic infrastructure will require the ability to solve large scale problems in real-time with hundreds of thousands of DERs simultaneously online. Because of the scale of the optimization problem, we use an iterative distributed algorithm previously developed in our group to operate each DER independently and autonomously within this environment. The algorithm is deployed within a framework that allows the microgrid to dynamically adapt to changes in the operating environment. Specifically, we consider a commercial site equipped with on-site PV generation, partially curtailable load, EV charge stations and a battery electric storage (BES) unit. The site operates as a small microgrid that can participate in the wholesale market on the power grid. We report results for simulations using real data that demonstrate the ability of the optimization framework to respond dynamically in real-time to external conditions while maintaining the functional requirements of all DERs.

Solar cells

The history, state of the art and future prospects of the direct generation of electricity from sunlight are reviewed. Although expensive, photovoltaic solar cells have become progressively cheaper over the past five years and are already cost-effective for a wide range of low-power applications. The potential economic and social benefits of these devices are such that intensive and extensive efforts are being made to cut costs still further and encourage market growth. During the 1980s, silicon solar cell systems are expected to become cheaper to run than diesel generators. As a result, they are likely to be adopted for water pumping, irrigation and rural electrification, especially in developing countries. At a later stage, on-site photovoltaic generation may be used for houses, offices, hospitals, schools, shopping centres and factories. But a technological breakthrough to a highly-efficient thin-film cell is needed before photovoltaics can have any significant impact on the large-scale generation of electricity in industrialised countries.