1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown.

Solar PV has seen a spectacular market development in recent years and has become a cost competitive source of electricity in many parts of the world. Yet, prospective observations show that the coronavirus pandemic could impact renewable energy projects, especially in the distributed market. Tracking and attributing the economic footprint of COVID-19 lockdowns in the photovoltaic sector poses a significant research challenge. Based on millions of financial transaction records and 44 thousand photovoltaic installation records, we tracked the spatio-temporal sale network of the distributed photovoltaic market and explored the extent of market slowdown. We found that a two-month lockdown duration can be assessed as a high-risk threshold value. When the lockdown duration exceeds the threshold value, the monthly value-added loss reaches 67.7%, and emission reduction capacity is cut by 64.2% over the whole year. We show that risks of a slowdown in PV deployment due to COVID-19 lockdowns can be mitigated by comprehensive incentive strategies for the distributed PV market amid market uncertainties.

Power optimization of a photovoltaic system with artificial intelligence algorithms over two seasons in tropical area.

Power output from the PV module changes continuously with time depending upon the climatic condition. This changes are most important in tropical area like Senegal due to the variation of the seasons (dry and rainy). Furthermore, different types of maximum power point tracking (MPPT) algorithm are presented in literature in order to get maximum output from the PV system. They can be summarized in two categories: classical and intelligent methods. The classical methods in no uniform weather condition are not efficient and an important loss of energy is showed. However, faced to this problematics like energy loss and no uniform weather conditions an Adaptative methods is used to optimize the PVs energy. In this study, two intelligent controllers based on artificial neural networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) are proposed to optimize the PVs production in non-uniform weather conditions and compared in order to show the most powerful model. For the ANN, the main challenge is to find the optimal neural in the hidden layer and in the paper, it is obtained using evaluator factor like mean squared error (MSE). These techniques using artificial intelligence (AI) algorithms are used for power optimization of a photovoltaic system are trained and validated with real data from a photovoltaic micro power plant in dry and rainy season installed at polytechnic high school of Dakar. The performances of the controllers to optimize the PVs power are evaluated during the dry and rainy seasons. Simulation results show that the ANFIS MPPT controller is more efficient and robust than ANN in non-uniform weather conditions. They have the ability of generalization and adaption to each meteorological conditions. These bullet summarize the applied methodology•A real electrical characteristics of photovoltaic panel are used for learning and validation of the controllers.•A comparative study of the methods in two different season is done.•ANFIS gives best performance in weather conditions compared to the ANN.

Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization.

Regardless of their nature of stochasticity and uncertain nature, wind and solar resources are the most abundant energy resources used in the development of microgrid systems. In microgrid systems and distribution networks, the uncertain nature of both solar and wind resources results in power quality and system stability issues. The randomization behavior of solar and wind energy resources is controlled through the precise development of a power prediction model. Fuzzy-based solar PV and wind prediction models may more efficiently manage this randomness and uncertain character. However, this method has several drawbacks, it has limited performance when the volumes of wind and solar resources historical data are huge in size and it has also many membership functions of the fuzzy input and output variables as well as multiple fuzzy rules available. The hybrid Fuzzy-PSO intelligent prediction approach improves the fuzzy system's limitations and hence increases the prediction model's performance. The Fuzzy-PSO hybrid forecast model is developed using MATLAB programming of the particle swarm optimization (PSO) algorithm with the help of the global optimization toolbox. In this paper, an error correction factor (ECF) is considered a new fuzzy input variable. It depends on the validation and forecasted data values of both wind and solar prediction models to improve the accuracy of the prediction model. The impact of ECF is observed in fuzzy, Fuzzy-PSO, and Fuzzy-GA wind and solar PV power forecasting models. The hybrid Fuzzy-PSO prediction model of wind and solar power generation has a high degree of accuracy compared to the Fuzzy and Fuzzy-GA forecasting models. The rest of this paper is organized as: Section II is about the analysis of solar and wind resources row data. The Fuzzy-PSO prediction model problem formulation is covered in Section III. Section IV, is about the results and discussion of the study. Section V contains the conclusion. The references and abbreviations are presented at the end of the paper.

Data for the modelling of the future power system with a high share of variable renewable energy.

Energy and power system models have become necessary tools that provide challenges and technical and economic solutions for integrating high shares of Variable Renewable Energy. Models are focused on analysing strategies of power systems to achieve their decarbonisation targets. The data presented in this paper includes the model algorithm, inputs, equations, modelling assumptions, supplementary materials, and results of the simulations supporting the research article titled "Facing the high share of variable renewable energy in the power system: flexibility and stability requirements". The analysis is based on data from the system operator of one of the European Union member states (Spain). The developed model allows making projections and calculations to obtain the power generation of each technology, the international interconnections, inertia, emissions, system costs and flexibility requirements of new technologies. These data can be used for energy policy development or decision making on power capacity and the balancing needs of the future power system.

Hot stuff: Research and policy principles for heat decarbonisation through smart electrification.

There is a need for major greenhouse gas emission reductions from heating in order to meet global decarbonisation goals. Electricity is expected to meet much of the heat demand currently provided by fossil fuels in the future and heat pumps may have an important role. This electrification transformation is not without challenges. Through a detailed narrative review alongside expert elicitation, we propose four principles for heat decarbonisation via electrification: putting energy efficiency first, valuing heat as a flexible load, understanding the emission impacts of heat electrification and designing electricity tariffs to reward flexibility. As a route to heat decarbonisation, when combined, these principles can offer significant consumer and carbon reduction benefits. In the short term these principles can encourage the smooth integration of heat electrification and in the longer term these principles are expected to reduce the scale of required infrastructural expansion. We propose a number of policy mechanisms which can be used to support these principles including (building) regulation, financial support, carbon standards, energy efficiency obligations and pricing.

Effect of COVID-19 virus on reducing GHG emission and increasing energy generated by renewable energy sources: A brief study in Malaysian context.

Coronavirus 2019 (COVID-19) has globally affected the human mortality rate and economic history of the modern world. According to the World Health Organization, COVID-19 has caused a severe threat to the health of the vulnerable groups, notably the elderly. There is still some disagreements regarding the source of the virus and its intermediate host. However, the spread of this disease has caused most countries to enforce strict curfew laws and close most industrial and recreational centres. This study aims to show the potential positive effects of COVID-19 on the environment and the increase of renewable energy generation in Malaysia. To prevent the spread of this disease, Malaysia enacted the Movement Control Order (MCO) law in March 2020. Implementation of this law led to a reduction in environmental pollution, especially air pollution, in this country. The greenhouse gases (GHG) emission , which was 8 Mt CO2 eq. from January 2020 to March 2020, reduced to <1 Mt CO2 eq. for April and May. The reduction of GHG emission and pollutant gases allowed more sunlight to reach photovoltaic panels, hence increasing the renewable energy generation.

Towards a large-scale integration of renewable energies in Morocco.

Renewable energies are a sustainable, unlimited and decarbonised solution to address future energy challenges. In this context, Morocco has a considerable advantage to position itself on this promising market. Furthermore, renewable energies have been highlighted as a key strategic source for the country's green growth. Morocco has adopted the renewable energy path through a strategy targeted on the development of solar, wind and hydroelectric power to boost its energy policy by adapting it to the challenges posed by today's world. Nowadays, Morocco is facing a challenge to reach 52% by 2030 of its total renewable energy capacity, which will exceed 42% by the end of 2020. The main objective of this paper is to study a scenario for 2030 for the Moroccan electricity system and to identify the challenges that need to be addressed in order to accelerate the integration of renewable energies in the Moroccan energy mix and to achieve a possible export of such green energy towards Europe.