Local Photovoltaic Generation Research Articles

Multi-objective flexibility disaggregation to distributed energy management systems

Flexibility from stationary batteries and electric vehicles allow home energy management systems to optimally consume local photovoltaic generation. The aggregated flexibility potential of a set of energy management systems can further contribute to a common objective, e.g., peak shaving at the transformer station. This paper formalizes the (non-convex) goals of involved stakeholders when disaggregating a flexibility request to a pool of energy management systems as multi-objective problem. The proposed genetic algorithm optimizes for the resource aggregator ( minimizing cost and maximizing delivery probability ), the grid operator ( minimizing grid losses ), the resource provider ( maximizing autarky and self-consumption ), and a fairness provider ( maximizing fairness among participants ). A disaggregation schedule that satisfies the objectives of all stakeholders can be chosen a posteriori from the Pareto-optimal solution set, which is demonstrated with a case study on the IEEE 906 low voltage test feeder.

Utility-Scale Storage Integration in the Maltese Medium-Voltage Distribution Network

Deployment of renewable energy sources in Malta is limited by grid integration constraints. Photovoltaic (PV) systems pose a significant risk to grid stability due to their inherent intermittency and result in overvoltages at the medium-voltage and low-voltage networks. Investments in utility-scale battery energy storage systems (BESS) will facilitate further deployment of renewables and will help to achieve energy security. This study proposed a novel sizing strategy for utility-scale battery energy storage systems (BESS) based only on technical considerations to find the minimum required storage capacity based on historical electricity demand and PV generation. The modeling and simulation were constrained to a section of the Gozitan 11 kV electrical distribution network and the results showed that the utility-scale storage can reduce the impact of PV systems on the grid infrastructure by avoiding reverse power flows and improve the local energy security by reducing the peak electricity demand. The central BESS and the decentralized coordinated BESS with “equal sizing” stored 3.4 MWh while the decentralized coordinated BESSs with “optimal sizing” stored 5.307 MWh. In all three cases, the evening peak demand was reduced by 30.5% from 2.62 MW down to a defined limit of 1.82 MW. From the results presented in this paper, the “optimal sizing” strategy showed that the BESSs have most benefit when installed next to the local PV generation. Hence, by deploying coordinated utility-scale BESSs sized according to the PV generation potential, it is expected that the penetrations of PV generation can be increased even with the present distribution network infrastructure.

Transforming Romanian school buildings in prosumers. An opportunity for increasing the energy efficiency in electrical networks and reducing the environmental impact of the power distribution sector

In the last decades, electricity systems suffer crucial transformations from integrated networks with a unidirectional flow, to decentralized, bidirectional flow networks. The stakeholders of this large-scale business have changed or improved their role. Electricity distribution companies are becoming service providers and end-users are becoming electricity producers. With the legislation issued in 2018, a new stakeholder was born on the Romanian electricity market, the prosumer. So, the decentralization reached an extreme by giving the smallest end-user the opportunity to become an electricity producer. In the first paragraph of this paper, authors summarize provisions of most recent energy strategies issued by the Romanian Government and the regulatory framework for incentivizing this market player. Given the growing interest of local administration in energy efficiency and pollution reduction, authors consider that schools can play a major role in fulfilling ambitious environmental targets. Most school buildings date from the communist regime and have a rather similar design. In their energy management programmes, municipalities consider energy efficiency in school buildings and authors present the case study of a school in the city of Ploiesti, one of the largest in southern Romania. A brief description of the electricity end-use systems is presented. Before any measure is taken on the supply-side, school’s administration needs to implement the demand-side measures. Next, these measures are investigated, and the results of the technical-economic analysis are presented. In southern Romania, photovoltaic (PV) solutions have better operating conditions and for this case study the solution for local generation with PV panels is presented. In the paper, authors are making an in-depth investigation of extending the prosumers model, with local PV generation on school buildings, over most of Romanian geographical regions. The carbon footprint is calculated, and the environmental benefits of this integrated approach are highlighted.

Distributed energy storage system scheduling considering tariff structure, energy arbitrage and solar PV penetration

We develop a new convex optimization (CO)-based charge/discharge scheduling algorithm for distributed energy storage systems (ESSs) co-located with solar photovoltaic (PV) systems. The CO-based scheduling algorithm minimizes the monthly electricity expenses of a customer who owns an ESS and incorporates both a time-of-use volumetric tariff and a demand charge tariff. Further, we propose the novel idea of a “supply charge” tariff that incentivizes ESS customers to store excess solar PV generation that may otherwise result in reverse power flow in the distribution grid. By means of a case study we observe the CO-based daily charge/discharge schedules reduce (1) peak net demand (that is, load minus PV generation) of the customer, (2) power fluctuations in the customer net demand profile, and (3) the reliance of the customer on the grid by way of promoting energy self-consumption of local solar PV generation. Two alternate methods for behind-the-meter ESS scheduling are considered as benchmarks for cost minimization, peak net demand reduction, and mitigation of net demand fluctuations. The algorithm is tested using real 30-min interval residential load and solar data of 53 customers over 2-years. Results show that the CO-based scheduling algorithm provides mean peak net demand reductions between 46% and 64%, reduces mean net demand fluctuations by 25–49%, and increases the mean solar PV self-consumption between 24% and 39% when compared to a customer without an ESS. Introduction of a supply charge reduces the maximum solar PV power supply to the grid by 19% on average and does not financially impact ESS owners.

Cost optimal sizing of smart buildings' energy system components considering changing end-consumer electricity markets

Managing the electricity system becomes increasingly challenging, calling for modifications of the current electricity market. High fluctuations in power generation could make the introduction of dynamic end-consumer electricity pricing reasonable. Furthermore, the prediction of end-consumers’ power consumption would get easier when charging the maximum power capacity, instead of the consumed energy. Thus, this paper discusses the capability of smart buildings to cope with such market models and evaluates how the design of the electrical and thermal energy system of a modern German building is affected. Therefore, cost optimal sizing of the main supply system components is carried out based on a hybrid MILP and a heuristic optimization algorithm. The results indicate that local photovoltaic generation is beneficial in almost all market conditions, while except for the capacity market, batteries are only economical if prices decrease by more than 60%. The identified electricity price dynamics are too low to incentivize investments into load shifting capable supply or storage systems. Nevertheless, if an installed heat pump and the associated thermal storage have smart home capabilities, they support the maximization of PV self-consumption and reduce electricity cost.

Capacity Controlled Demand Side Management: A Stochastic Pricing Analysis

We consider a novel paradigm for demand side management, assuming that an aggregator communicates with a household only at the meter, imposing a capacity constraint, i.e., a restriction on the total power consumption level within a given time frame. Consumers are then responsible to adjust the set-points of the individual household devices accordingly to meet the imposed constraint. We formulate the problem as a stochastic household energy management program, with stochasticity arising due to local photovoltaic generation. We show how a demand bidding curve for capacity increments can be constructed as a by-product of the developed problem and provide a rigorous pricing analysis that results in a probabilistic “shadow” price envelope. To evaluate the efficacy of the proposed approach, we compare it with an idealized real-time market price set-up and show how our analysis can provide guidelines to consumers when selecting a service contract for load curtailment.

Grid Impact Indicators for Active Building Simulations

This paper presents grid impact indicators, developed to evaluate the performance of local control mechanisms, affecting the impact of a net-zero energy building on the electricity grid, without the need to simulate the grid itself. The capacity factor, loss-of-load probability, cover factor for supply and demand, load match index, power exchange variability, one-percent peak power, peaks above limit, dimensioning rate, and kVA credit are proposed and applied to example single-building simulations. These simulations include control mechanisms affecting the power exchange of the building with the grid by adjusting the set-point temperature of the thermal energy storage tank of the heat pump, shifting local consumption to better coincide with local photovoltaic generation, or by directly limiting the power supplied to the grid. The simulations focus on a single net-zero energy building and use one such building as a reference and others for comparison of trends in simulation results. All modeling is done in the equation-based object-oriented modeling language Modelica. Simulations are performed using the Dymola environment.

Improving photovoltaics grid integration through short time forecasting and self-consumption

The uncertainty associated to the forecast of photovoltaic generation is a major drawback for the widespread introduction of this technology into electricity grids. This uncertainty is a challenge in the design and operation of electrical systems that include photovoltaic generation. Demand-Side Management (DSM) techniques are widely used to modify energy consumption. If local photovoltaic generation is available, DSM techniques can use generation forecast to schedule the local consumption. On the other hand, local storage systems can be used to separate electricity availability from instantaneous generation; therefore, the effects of forecast error in the electrical system are reduced. The effects of uncertainty associated to the forecast of photovoltaic generation in a residential electrical system equipped with DSM techniques and a local storage system are analyzed in this paper. The study has been performed in a solar house that is able to displace a residential user’s load pattern, manage local storage and estimate forecasts of electricity generation. A series of real experiments and simulations have carried out on the house. The results of this experiments show that the use of Demand Side Management (DSM) and local storage reduces to 2% the uncertainty on the energy exchanged with the grid. In the case that the photovoltaic system would operate as a pure electricity generator feeding all generated electricity into grid, the uncertainty would raise to around 40%.