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Abstract Electric grid is vulnerable to power imbalance and inertia is the grid's response to overcome such disturbance. Augmentation of power electronic converter based renewable energy technologies like Photovoltaic Generators (PVG) and batteries in utility grid significantly reduces inertia. Inertia degradation is indicated by sharp Rate of Change of Frequency (ROCOF) events due to any grid component failure or imbalance. Fixed gain feedback Proportional Integral Derivative (PID) control is insufficient to deal with varying ROCOF events. This work proposes Sliding Mode (SM) robust droop control scheme assisted by Artificial Neural Network (ANN) algorithm for an islanded PVG integrated microgrid. Droop response is governed by swing equation that uses PVG Maximum Power Point (MPP) forecasted by ANN. ANN forecast is compared with optimized Gaussian process regression algorithm based on mean squared error and speed of training as key performance indicator. The algorithms are trained and validated based on climate dataset of Islamabad, Pakistan. SM control performance is compared with various PID gain settings and qualified as the most suitable against variable source, load and ROCOF scenarios. Finally, significance of accurate MPP forecast for droop control is established by comparing the ANN and deterministic forecaster assisted droop response in a microgrid case study.

This paper presents a stochastic expansion planning model for coordinated natural gas and electricity networks, incorporating gas-fired generators, Power-to-Gas facilities, and renewable power sources. The primary objective is to minimize the total cost over the planning horizon, addressing the significant interdependencies between these networks which, if planned independently, can lead to higher overall costs. The originality of this work lies in its comprehensive integration of both systems, leveraging their synergies to optimize infrastructure investment and operational efficiency. Methodologically, the model employs mixed integer linear programming (MILP) within the General Algebraic Modelling System (GAMS), using a Scenario Tree concept to account for the stochastic nature of renewable energy sources (RESs) and load variations. Data from an adapted twenty-node Belgium gas network and a sixteen-bus UK electricity distribution system were utilized. Results demonstrate substantial cost savings and improved system performance with the integrated approach, validating the model’s effectiveness.

This paper proposes a novel approach for the reliability cost-based optimization of Distribution Systems (DS), considering tie line-based network reconfiguration method with integration of Distributed Energy Resources (DER). An optimal Energy not Supplied (ENS) index is proposed, where the capacity is handled by curtailment devices in the network such as sectionalizers and the energy supplied by DERs which considers Flexibility Services (FS) within a market environment. The decision variables include the investment and operation of tie-lines and buying regulation services from DER such as Distributed Generation (DG) and Battery Energy Storage Systems (BESS). The results validate the cost-effectiveness of the proposed method through implementation of these technologies to improve the reliability of the DS, within a comprehensive set of case-study scenarios for a 16-bus UK generic distribution system (UKGDS). The case study results indicate that significant savings can be achieved through the proposed method, ranging between 36%–71% depending on the level of automation in tie-line operations in combination with the settlement price for the power-balance of FS. This illustrates that the proposed DS reliability cost-based optimization method could have a significant impact for real world DG and BESS applications.

The importance of accurate forecasting in the electric sector has grown due to the increasing demand and adoption of high volume of Renewable Energy Sources (RES). Short-term forecasting (STF) using deep learning methods has shown potential for improving forecasting accuracy. However, the accuracy of these methods can be further enhanced by combining them to generate a hybrid model, selecting appropriate input features, generating new features, and optimizing model parameters. This paper proposes a novel multi-stage framework for PV and load STF that employs feature generation, feature selection, and optimal hyperparameter tuning preprocessing techniques. An enhanced hybrid CNN-LSTM deep learning model architecture is developed in the final stage of the proposed framework. The framework is assessed and compared to other leading-edge approaches across different DSO scenarios, including multiple single-phase residential loads, three-phase feeders, and secondary substation, demonstrating a significant reduction in forecasting errors.

This paper presents a comprehensive methodology for long-term planning in distribution networks to address the challenges associated with integrating renewable energy resources (RERs) and battery energy storage systems (BESSs). The methodology incorporates active network management (ANM) schemes, coordinated voltage control (CVC), and demand side management (DSM) to optimize the sizing and siting of RERs and BESS. Deep learning-based long-term forecasting techniques are utilized to capture the hourly variations in load demand, solar irradiance, and wind speed throughout the year. The objective is to minimize overall network costs, considering investment and operational costs, while planning over a 5-year horizon with a 3% annual load growth. The model includes tie-lines as a decision variable for network expansion investments, ensuring efficient network flexibility. The linearized mathematical model is developed as a mixed integer linear programming (MILP) problem to enhance scalability. A case study is conducted on the UKGDS 16-bus system to verify the optimization model, and further validation is performed on the IEEE 69-bus system to assess scalability. The results demonstrate the effectiveness of the proposed method in achieving accurate forecasting and efficient sizing and siting decisions for RERs and BESSs, including tie-line investments, providing valuable insights for real-world distribution network scenarios.

This paper looks into the issues around renewable energy with a view to identify the opportunities for Nigeria and critically review the nation's renewable energy policy vis-à-vis the efforts and achievement of governments and indigenous practitioners. It identified the inherent opportunities of renewable energy resources at ameliorating the incidents of climate change and global warming and also surveyed international statistics on the relationships between energy and renewable energy adoption, national development, population explosion, job creation and rural-urban integration. It found out that for Nigeria to sustain economic growth especially as it relates to agriculture and food security, renewable energy for power generation must be included in the nation's rural development plan. It also demonstrated that renewable energy poses an opportunity for mitigating the nation's contributions to anthropogenic climate change.

The increasing use of high shares of renewable energy sources (RESs) in the current electricity network introduces challenges to the design and management of the electricity network due to the variation and uncertainty nature of the RESs. Some existing energy infrastructures, such as heat, gas, and transport, all have some level of inbuilt storage capacity and demand response (DR) potentials that can be exploited in an energy system integration to give the electricity network some level of flexibility and promote an efficient transition to a low-carbon, resilient, and robust energy system. The process of integrating different energy infrastructure is known as multi-vector energy systems (MESs). This paper reviews different studies on the planning of MESs using the energy hubs (EHs) approach. The EHs model used in this paper links different energy vectors such as gas, electricity, and heat energy vectors in its planning model, as opposed to planning each energy vector independently, in order to provide more flexibility in the system, minimise total planning cost, and encourage high penetration of renewable energy source for future energy demands. In addition, different uncertainty modelling and optimization methods that have been used in past studies in planning of EH are classified and reviewed to ascertain the appropriate techniques for addressing RESs uncertainty when planning future EH. Numerical results show 12% reduction in the planning cost in the case of integrated planning with other energy vectors compared to independent planning.

This paper used different risk management indicators applied to the investment optimization performed by consumers in Distributed Generation (DG). The objective function is the total cost incurred by the consumer including the energy and capacity payments, the savings, and the revenues from the installation of DG, alongside the operation and maintenance (O&M) and investment costs. Probability density function (PDF) was used to model the price volatility in the long-term. The mathematical model uses a two-stage stochastic approach: investment and operational stages. The investment decisions are included in the first stage and which do not change with the scenarios of the uncertainty. The operation variables are in the second stage and, therefore, take different values with every realization. Three risk indicators were used to assess the uncertainty risk: Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Expected Value (EV). The results showed the importance of migration from deterministic models to stochastic ones and, most importantly, the understanding of the ramifications of every risk indicator.

This paper proposes a novel method for planning active distribution networks (ADNs) with the integration of an active network management (ANM) scheme using coordinated voltage control (CVC) through on-load tap changer (OLTC) transformers. The method was formulated as a security-constrained optimal power flow (SCOPF) problem to minimize total operational costs, which maximizes the utilization of renewable distributed generators (DGs) over a planning horizon. The ANM scheme was applied using OLTC to ensure safe operation and reduce voltage violations in the network. To analyse the impact of ANM, the planning problem was examined both with and without the ANM scheme. Moreover, SCOPF, considering the N-1 line contingency analysis and multi-DG configuration, was implemented to analyse the feasibility of the proposed method and the advantages of ANM under contingency situations. The method was validated on a weakly-meshed 16-bus UK generic distribution system (UKGDS). The results showed that ANM can lower operational costs and maintain network voltage for operation in feasible conditions even in the case of a contingency. Moreover, the ANM scheme mitigated the voltage rise effect caused by DGs and maximized their utilization.

The single-stage flyback Photovoltaic (PV) micro-inverter is considered as a simple and small in size topology but requires expensive digital microcontrollers such as Field-Programmable Gate Array (FPGA) or Digital Signal Processor (DSP) to increase the system efficiency, this would increase the cost of the overall system. To solve this problem, based on a single-stage flyback structure, this paper proposed a low cost and simple analog-digital control scheme. This control scheme is implemented using a low cost ATMega microcontroller built in the Arduino Uno board and some analog operational amplifiers. First, the single-stage flyback topology is analyzed theoretically and then the design consideration is obtained. Second, a 120 W prototype was developed in the laboratory to validate the proposed control. To prove the effectiveness of this control, we compared the cost price, overall system efficiency, and THD values of the proposed results with the results obtained by the literature. So, a low system component, single power stage, cheap control scheme, and decent efficiency are achieved by the proposed system. Finally, the experimental results present that the proposed system has a maximum efficiency of 91%, with good values of the total harmonic distortion (THD) compared to the results of other authors.

A fuzzy controller for improving Fault Ride-Through (FRT) capability of Variable Speed Wind Turbines (WTs) equipped with Doubly Fed Induction Generator (DFIG) is presented. The controller is designed in order to compensate the voltage at the Point of Common Coupling (PCC) by regulating the reactive and active power generated by WTs. The performances of the controller are evaluated in some case studies considering a different number of wind farms in different locations. Simulations, carried out on a real 37-bus Italian weak distribution system, confirmed that the proposed controller can enhance the FRT capability in many cases.