Optimal Phase Switch Device Placement Scheme for Low Voltage Distribution Network Considering Load and Renewable Energy Uncertainty

Day-ahead interval optimization of combined cooling and power microgrid based on interval measurement

Optimizing peak shaving operation in hydro-dominated hybrid power systems with limited distributional information on renewable energy uncertainty

Grid-connected photovoltaic system in Malaysia: A review on voltage issues

As renewable energy sources are becoming more widely integrated into the modern power system, the uncertainties within this system are becoming increasingly prominent. It is crucial to accurately describe the uncertainties in renewable energy output for the effective planning, scheduling, and control of power systems. For this purpose, the aim of this paper is to introduce a method for generating short-term output scenarios for renewable energy sources based on an improved Wasserstein Generative Adversarial Nets-Gradient Penalty. First, a Deep Neural Network structure inspired by the Transformer algorithm is developed to capture the temporal characteristics of renewable energy outputs. Then, combined with the advantage of the data generation of the Wasserstein Generative Adversarial Nets-Gradient Penalty, the Transformer–Wasserstein Generative Adversarial Nets-Gradient Penalty is proposed to generate short-term renewable energy output scenarios. Finally, experimental validation is conducted on open-source wind and photovoltaic datasets from the U.S. National Renewable Energy Laboratory, where the performance of the proposed model in generating renewable energy output scenarios across various aspects (i.e., individual sample representation, expectation and variance, probability density function, cumulative distribution function, power spectral density, autocorrelation coefficient, and pinball loss) is assessed. The results show that our method outperforms the Wasserstein Generative Adversarial Nets-Gradient Penalty, Variational Autoencoder, Copula function, and Latin Hypercube Sampling models in the abovementioned evaluation indicators, providing a more precise probability distribution representation of realistic short-term renewable energy outputs.

Virtual power plant (VPP) is an important way to solve the problem of distributed generation participating in power market transaction. However, the uncertainty of distributed renewable energy output brings high transaction risk to virtual power plant. For the VPP with renewable energy permeability, considering the uncertainty of renewable energy output, load and market price as well as the influence of demand-side resource participation in the power market, this paper puts forward the demand response bidding strategy of VPP under the open environment of electricity selling side. A multi-stage bidding strategy model is built for VPP participating in day-ahead energy market, intraday demand response market and real-time energy market, and solved based on stochastic programming theory. The results of the examples can support the effectiveness of the model and provide decision support for the participation of distributed energy and virtual power plants in power market transactions under the background of power system reform in China.

The transition to a low carbon society is dependent on renewable energy-based electrification. Nevertheless, energy programs have resulted in growing societal polarization in several regions. Therefore, around the globe, government and legislative authorities at the local, regional, national, and international levels are highly concerned about the environmental impacts and risk factors that influence the energy paradigm. Thus, to minimize and/or limit the environmental insecurity issues, the world needs swift and effective actions to secure the climate for a better tomorrow. Moreover, there is a dire need to look for new energy alternatives, along with reducing the complete dependence on petro-based energy sources. Keeping this burning issue in mind, herein, an effort has been made to present the potential of several renewable energy sources, including wind-based renewable energy, solar-based renewable energy, hydro-based renewable energy, and biomass-based renewable energy. Following a brief introduction of energy-related problems and opportunities, a comparative overview is given to renewable vs. non-renewable energy sources. Then, several renewable energy sources, including wind, solar, hydro, and biomass, along with the worldwide energy capacity of each energy source are given with suitable examples and statistics. Finally, risk factors, concluding remarks, and future guidelines are discussed towards the end.

Concerns over changes to the global environment and the growing need for energy have increased the penetration of renewable energy (RE) generation into low voltage distribution networks. The introduction of Energy Storage Systems (ESS) into distribution networks has been proposed to improve the reliability and performance of power systems. Energy storage systems will also be important in helping to regulate the supply from intermittent RE sources that feed into variable load demand. The focus of this paper is to provide an overview of the state of the art of ESS, concentrating on the distinct characteristics and applications of the different types. The analysis includes comparison and evaluation of different storage preferences with regard to integration of these technologies with an electric grid at the distribution network level that may lead to benefits such as distribution upgrade deferral and improvements in power quality and reliability.

Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal fusion-based predictive tool for energy consumption prediction (MDLFM-ECP) of RE and NRE power sources. Actual data may influence the prediction performance of the results in prediction approaches. The proposed MDLFM-ECP technique involves pre-processing, fusion-based prediction, and hyperparameter optimization. In addition, the MDLFM-ECP technique involves the fusion of four deep learning (DL) models, namely long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), deep belief network (DBN), and gated recurrent unit (GRU). Moreover, the chaotic cat swarm optimization (CCSO) algorithm is applied to tune the hyperparameters of the DL models. The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models, showing the novelty of the work. A series of simulations took place to validate the superior performance of the proposed method, and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

Renewable energy is the fundamental approach to reducing the carbon emission of power systems. To fully utilize renewable energy sources in the planning stage, a co-optimization of generation and transmission expansion planning (G&TEP) strategy is proposed, where the system security is guaranteed under uncertainties. To address the long-term uncertainties of loads, renewable energy output, and component failures across the planning horizon, a novel decision-dependent ambiguity set is proposed using total variation distance, where the renewable energy output and component failures are affected by planning. A multi-year G&TEP problem is formulated as a two-stage distributionally robust optimization problem with decision-dependent ambiguity sets, where the renewable energy, energy storage systems (ESSs), and transmission lines are jointly optimized. Using Lagrange duality, this problem is further reformulated as a mixed-integer linear programming problem, which can be solved by the off-the-shelf solvers. The simulations are performed on a modified Garver 6 test system. The effectiveness of the proposed strategy is verified by the numerical results.

The multi energy complementary system is a new power energy technology Firstly, we studied renewable energy and load uncertainties of an operation optimization system, and established the industrial park energy system, which includes wind power, photovoltaic power, a combined cooling, heating and power system, and an energy storage tank. Secondly, given the renewable energy uncertainties of unit output and load, we introduced a robust multi-objective operation optimization method for industrial park energy supply systems while considering conservative system operation. Thirdly, we examined the synergetic and game relationship among multiple objectives. The particle swarm optimization algorithm is was used to optimize the system operation scheme, reduce the feasible domain, and improve the efficiency of the solution. Finally, the simulation results show that the operation optimization method effectively uses the demand response to optimize economic and environmental objectives and ensure the optimal operation efficiency of the system under multiple uncertainties.

The renewable energy sources such as wind power and solar energy are widely connected to the current power network. The proportion of the renewable resources to the entire energy sources in the power grid is increasing. Since wind power and solar energy are intermittent, which have more uncertain factors, power grid operation is being impacted by generation of the renewable energy sources. Especially in the area of power balance and dispatch. Severe power imbalance, which causes the huge frequency deviation, will lead to the stability of power system. Therefore, it is very important to deal with the uncertainties of renewable energy. The existing deterministic approaches are not sufficient to deal with the uncertain factors. This paper analyzes the effect of renewable energy uncertainties in power grid operation based on the fuzzy theory and probabilistic methods. The steady-state security analysis is taken as an example to handle renewable energy uncertainty in power grid operation.

This paper examines the long-term and short-term relationships between renewable energy consumption, output and export, and CO2 emissions in China over the period 1990–2020 from the perspective of industry and agriculture using econometric methods. The results of the study found that there is a long-run relationship and there is a causality between these variables, indicating that renewable energy consumption, output, and export are related to CO2 emissions. Specifically, from a long-term perspective, the results of co-integration and causality reveal that there is a two-way causal relationship between renewable energy consumption, output, export, and CO2 emissions, supporting the feedback hypothesis; that is, output and export have an adverse impact on the environment, while renewable energy consumption has a favorable impact on the environment. In the short term, there is a direct or indirect one-way causal relationship between export, CO2 emissions, and renewable energy consumption, which supports the growth hypothesis. The impulse response analysis has further verified the causality test results and supported this hypothesis. However, there is a strong negative correlation between industrial and agricultural export and renewable energy consumption, which will cause the use of renewable energy to fail to meet the peak demand for industrial and agricultural export in the short term. Conversely, large amounts of fossil fuels will be consumed to meet output and export demand. Therefore, on the road to social, economic, and environmental sustainability, it is necessary to consider the impact of economic growth and energy consumption (renewable and non-renewable energy) of related industries on CO2 emissions, which also provides a strong basis for the development and reduction of China’s renewable energy and the long-term implementation of the emission control policy.

Researchers have found that despite a wide range of renewable energy sources in sub-Saharan Africa (SSA), renewable energy pricing policies have focused extensively on metered electricity energy, an early source of renewable energy. Supply, access, and regulation of price for metered electricity energy is mostly controlled by the governments across SSA. There is an increasing use of other renewable energy sources including portable electricity, solar power, and wind power. However, in SSA, the pricing for domestic renewable domestic renewable power such as portable electricity, rechargeable cookstoves, and portable solar power sources are left to the market to legislate, with energy prices dependent on forces of demand and supply and seldom on clear scientific models. This commercially focused energy market means businesses operating in the energy industry are more interested in profits and set prices relative to their market perceptions. The main problem with the energy market in SSA is the lack of a participatory approach where customers, businesses, the government, and other stakeholders are involved in the pricing for energy. We further note that lack of a participatory approach in energy pricing is a major challenge in uptake and demand for the domestic renewable energy sources. Through a systematic literature review, including a review of peer-reviewed journals, documents from energy utility companies, and published information on the websites for energy companies, this review analyzes the current application of energy price modeling and hypothesizes that mobile technology and a participatory pricing approach can improve pricing for domestic renewable power. Our initial literature review showed that energy price modeling had received little attention in SSA, especially for domestic renewable power energy sources. This paper, therefore, fills this gap by using a systematic literature review to consolidate knowledge on how energy price modeling has been applied in the SSA context. The systematic literature review results reveal four commonly used models: time series, artificial neural network, hybrid iterative reactive adaptive, and hybrid models. These energy pricing models are mainly applied to metered electricity power, the predominant source of energy in SSA. The literature hypothesizes that applying mobile technology to energy pricing and a participatory approach involving the consumers and energy supply businesses can move SSA closer to transitioning to renewable energy. Although other factors have hindered this transition, a participatory energy pricing approach incorporating relevant pricing models and market information creates potential solutions to these challenges. In the discussion, we hypothesize that a participatory approach to price modeling with the incorporation of mobile technology can be used at the household level to improve energy decision-making. For this to work, energy price modeling for domestic renewable sources should be simplified, user-friendly, and accessible to households. In conclusion, we recommend that SSA governments develop a more holistic view of energy price modeling to better harness the potential for domestic renewable energy sources.

ABSTRACTThe growing demand for energy in developing societies, coupled with dwindling conventional resources, necessitates the adoption of renewable energy (RE) sources. A comprehensive understanding of these sources and their optimal utilization is crucial. The awareness of the users plays a very important role in the effective utilization of different energy sources. Hence, this study examines the level of RE awareness in Palestine, identifying factors influencing public perception and exploring potential strategies for promoting RE adoption. This awareness depends on different factors related to the users' education level, community type, and type of building. Different factors that affect the willingness and attitude to gain knowledge about RE are presented. In addition, various practices highlighting the increasing level of awareness regarding the adoption of RE sources are investigated. In addition, the status of RE education and its potential impact on engineers have also been discussed. Various individual and institutional efforts are highlighted in the study as strategies to bridge the gap and overcome obstacles to adopting RE sources. Suggestions for better use of these resources have been highlighted. Thereafter, recommendations for effective RE implementation in Palestine are analyzed and conclusions are drawn. The main challenges in integrating RE into Palestine's existing energy grid and system include infrastructure limitations, resources and financial constraints, and political issues. Still, there are possibilities in the form of international collaboration and investment. A flexible and modular RE grid could also address some of these challenges by integrating small‐scale systems that can be scaled up as needed.

Optimizing the technical and economic value of energy storage systems in LV networks for DNO applications