Various demand side management techniques and its role in smart grid–the state of art

The current lifestyle of humanity relies heavily on energy consumption, thus rendering it an inevitable need. An ever-increasing demand for energy has resulted from the increasing population. Most of this demand is met by the traditional sources that continuously deplete and raise significant environmental issues. The existing power structure of developing nations is aging, unstable, and unfeasible, further prolonging the problem. The existing electricity grid is unstable, vulnerable to blackouts and disruption, has high transmission losses, low quality of power, insufficient electricity supply, and discourages distributed energy sources from being incorporated. Mitigating these problems requires a complete redesign of the system of power distribution. The modernization of the electric grid, i.e., the smart grid, is an emerging combination of different technologies designed to bring about the electrical power grid that is changing dramatically. Demand side management (DSM) allow customers to be more involved in contributors to the power systems to achieve system goals by scheduling their shiftable load. Effective DSM systems require the participation of customers in the system that can be done in a fair system. This paper focuses primarily on techniques of DSM and demand responses (DR), including scheduling approaches and strategies for optimal savings.

A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters

In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probability of utility pole failure through conventional hydrological, hydrostatic, and geotechnical calculations embedded to a unique mixed-integer linear programming (MILP) optimization framework. Once the flow rates that cause utility pole overturn are determined, the most cost-efficient subterranean pipe network configuration can be created that will allow for flood waters to be redirected from vulnerable infrastructure elements. The optimization framework was simulated using the Julia scientific programming language, for which the JuMP interface and Gurobi solver package were employed to solve a minimum cost network flow objective function given the numerous decision variables and constraints across the network. We implemented our optimization framework in three different watersheds across the U.S. These watersheds are located near Whittier, NC; Leadville, CO; and London, AR. The implementation of a minimum cost network flow optimization model within these watersheds produced results demonstrating that the necessary amount of flood waters could be conveyed away from utility poles to prevent failure by flooding.

Simulation of Electric Vehicle Charging Points Based on Efficient Use of Chargers and Using Recuperated Braking Energy from Trains

Electric vehicles represent an innovation in mobility that can help significantly reduce greenhouse emissions and mitigate climate change. However, replacing internal combustion with electric vehicles is not enough. This replacement needs to be complemented with a change in the energy mix of individual countries towards renewable energy sources and efficient use of electricity generated as a secondary product. Recuperative braking energy from trains can serve as one source of such secondary energy. Following an analysis of recuperative energy generated and analysis of charging requirements of individual electric vehicles, the paper proposes a model of a charging site near train stations. Using this energy to charge electric vehicles helps to reduce energy consumption from the electricity grid and thus reduce carbon emissions. Compared to other articles, the proposed model ensures the efficient use of recuperative braking energy from trains by using the variable charging power function; thereby, the installation of additional battery storage is eliminated. Our model results show that the benefits of a car park with a reservation system near train stations increase the car park efficiency, provide a sufficient number of private charging points, contribute to efficient use of recovered energy, and reduce carbon emissions.

Machine Intelligent Hybrid Methods Based on Kalman Filter and Wavelet Transform for Short-Term Wind Speed Prediction

Wind power’s increasing penetration into the electricity grid poses several challenges for power system operators, primarily due to variability and unpredictability. Highly accurate wind predictions are needed to address this concern. Therefore, the performance of hybrid forecasting approaches combining autoregressive integrated moving average (ARIMA), machine learning models (SVR, RF), wavelet transform (WT), and Kalman filter (KF) techniques is essential to examine. Comparing the proposed hybrid methods with available state-of-the-art algorithms shows that the proposed approach provides more accurate prediction results. The best model is a hybrid of KF-WT-ML with an average R2 score of 0.99967 and RMSE of 0.03874, followed by ARIMA-WT-ML with an average R2 of 0.99796 and RMSE of 0.05863 over different datasets. Moreover, the KF-WT-ML model evaluated on different terrains, including offshore and hilly regions, reveals that the proposed KF based hybrid provides accurate wind speed forecasts for both onshore and offshore wind data.

Modeling of Risk Aversion Linked to Renewable Energy Policy and Decision-Maker Behavior

This paper presents the behavior of decision makers, the possible choices and the strategies 1 resulting from the uncertainties related to the integration of renewable energies. Its uncertainties 2 are the risks associated with the volatility of renewable sources, the dynamics of energy production 3 as well as the planning and operation of the electricity grid. The goal is to model the risk-averse 4 decision-maker’s behavior and the choice of integrating renewable energies into the electrical system. 5 Following a bibliographic approach, we expose a methodology to model the decision-maker’s 6 behavior(risk aversion and predilection for risk) to risk taking. The risk-averse decision maker may 7 adopt nonlinear utility functions. Risk aversion is a behavior that reflects the desire to avoid risk 8 decisions and thus reduces the risk of adverse consequences. A decision support tool is provided to 9 the decision-maker to choose a best-fit strategy based on his preferences. The rational and risk-averse 10 decision-maker would seek to maximize a concave utility function instead of seeking to minimize its 11 cost. Taste or aversion to risk can be modeled by a thematic function of utility.

Ducted Wind Turbine in Generating Set Air Flow

The growing need to use renewable sources and the current difficulty in spreading the electricity grid in a widespread manner raise the question of how to respond to the need for more electricity immediately. The idea behind this study is to power a horizontal axis wind turbine with the air flow generated for cooling a stationary internal combustion engine. The power extracted from this solution is significantly lower than that of the internal combustion engine (about 0.3%) and could be advantageous only in limited contexts. Installation costs are limited because many elements deriving from wind variability can be removed or simplified.

EU climate progress will be most clear in renewables

Significance Although the exact details of the package remain subject to clarification and amendment, it requires a much broader decarbonisation effort beyond the power sector, as well as public-sector financial commitments to higher-risk energy transition technologies such as hydrogen and carbon capture and storage. Impacts Increased renewable energy capacity and wider electrification will highlight lagging investment in electricity grid infrastructure. Enhanced offshore wind targets and European developers’ desire to enter foreign markets will stretch offshore wind supply chains. Although increasingly contentious, the role of gas is likely to become more important in terms of European security of energy supply.

Transition towards a full self-sufficiency through PV systems integration for sub-Saharan Africa: a technical approach for a smart blockchain-based mini-grid

Access to affordable, reliable and clean energy is an important sustainability goal of the United Nations. In areas where the public electricity grid is unreliable or unavailable, photovoltaic systems can be a solution. However, they are cost-intensive, mainly because of the energy storage systems. Mini-grids can be an answer for reducing upfront investment and overall system lifetime costs while increasing electricity availability. The mini-grid technology is mature, nevertheless, there are downsides when it comes to integrating existing solar systems of different manufacturers. The system topology is usually predefined and a central instance controls the mini-grid. Thus, the integration of existing power systems is difficult due to the communication constraints of these systems with the mini-grid controller. Including existing power systems into a decentralized mini-grid, can highly increase cost-efficiency. In a decentralized approach payments for the consumed energy between mini-grid actors are required. Accounting is, however, a complex administrative procedure, if the respective power systems are owned by different individuals and organizations. A transparent blockchain-based temper-proof approach can be a solution to automate metering and billing, allowing automatic payments between independent subsystem owners using smart contracts. In order to further optimize the smart mini-grid, an artificial intelligence learning algorithm for a dynamic electricity price needs to be developed. This smart and decentralized approach for building Mini-Grids is a novelty bringing solar systems one step closer to self-sufficiency. This paper describes how a smart mini-grid solution can be implemented using the Don Bosco Solar & Renewable Energy Center campus mini-grid in Tema, Ghana as a case study.