Photovoltaic (PV) power forecasting has the potential to mitigate some of effects of resource variability caused by high solar power penetration into the electricity grid. Two main methods are currently used for PV power generation forecast: (i) a deterministic approach that uses physics-based models requiring detailed PV plant information and (ii) a data-driven approach based on statistical or stochastic machine learning techniques needing historical power measurements. The main goal of this work is to analyze the accuracy of these different approaches. Deterministic and stochastic models for day-ahead PV generation forecast were developed, and a detailed error analysis was performed. Four years of site measurements were used to train and test the models. Numerical weather prediction (NWP) data generated by the weather research and forecasting (WRF) model were used as input. Additionally, a new parameter, the clear sky performance index, is defined. This index is equivalent to the clear sky index for PV power generation forecast, and it is here used in conjunction to the stochastic and persistence models. The stochastic model not only was able to correct NWP bias errors but it also provided a better irradiance transposition on the PV plane. The deterministic and stochastic models yield day-ahead forecast skills with respect to persistence of 35% and 39%, respectively.

As is known, a reduction in CO ${}_2$ emissions is closely related to the improvement of energy efficiency and the increasing use of renewable energy sources in building stock due to its high contribution to worldwide energy consumption. The retail sector has become particularly interesting in this sense, because commercial buildings are no longer just places where a variety of services are offered to customers. In fact, they can be beacons of energy efficiency. In this paper, we propose a predictive energy control strategy that, through the combination of production and demand forecasting, can effectively shave and shift the peak consumption of shopping malls equipped with battery energy storage systems (BESS). The adopted optimization strategy takes into account the variability of electricity tariffs over time, as is customary in some European countries. The performed energy and economic simulations based on the experimental data collected in an Italian shopping mall clearly highlight the benefits in terms of energy and economic savings. Moreover, the reported results lead to the conclusion that BESS management, photovoltaic (PV) generation, and peak switch strategies can have a reasonable pay-back investment time even for buildings with a large energy demand.

One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms’ generation is important both for the producers/traders to minimize the imbalance costs and for the transmission system operators to ensure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale photovoltaic (PV) plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of the forecast accuracy and accuracy improvement in the context of the Italian energy market’s regulatory framework. Through this study, we further point out several important criticisms of the Italian “single pricing” system that brings paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome the undesired effects of the current dispatching regulations.

We introduce firm solar forecasts as a strategy to operate optimally overbuilt solar power plants in conjunction with optimally sized storage systems so as to make up for any power prediction errors, and hence entirely remove load balancing uncertainty emanating from grid-connected solar fleets. A central part of this strategy is the plant overbuilding that we term implicit storage. We show that strategy, while economically justifiable on its own account, is an effective entry step to achieving least-cost ultra-high solar penetration where firm power generation will be a prerequisite. We demonstrate that in the absence of an implicit storage strategy, ultra-high solar penetration would be vastly more expensive. Using the New York Independent System Operator (NYISO) as a case study, we determine current and future costs of firm forecasts for a comprehensive set of scenarios in each ISO electrical region, comparing centralized vs. decentralized production and assessing load flexibility’s impact. We simulate the growth of the strategy from firm forecast to firm power generation. We conclude that ultra-high solar penetration enabled by the present strategy, whereby solar would firmly supply the entire NYISO load, could be achieved locally at electricity production costs comparable to current NYISO wholesale market prices.

Grid‐connected photovoltaic electricity production steadily grows at the margin of conventional power generation, but its management becomes more complex. To overcome this challenge, a transformation of variable renewable energy (VRE) resources into firm power generation is proposed. Drawing on insights from the International Energy Agency Photovoltaic Power System Task 16 case studies, it becomes evident that achieving nearly 100% VRE power grids that reliably meet demand year‐round can be economically viable through optimal VRE transformation. This transformation involves various traditional methods, e.g., storage, VRE blending, geographical dispersion, and load flexibility. However, overbuilding VRE capacity and controlled curtailment, acting as implicit energy storage, are now seen as essential prerequisites for this transformation. Nevertheless, aligning this vision with the current market rules poses a dilemma as it doesn't necessarily align with VRE producers’ interests. This predicament calls for a reconsideration of VRE market regulations. Current designs based on marginal energy production signals do not suffice. Instead, it is advocated for market rules grounded in the capacity of firmly enabled VREs rather than their energy output. Ultimately, the economic model should harmonize with the variability of VRE resources, rather than forcing VRE resources to adapt to existing market structures.