Technology advancement in the last few decades allows large penetration of renewable energy resources in the distribution network (DN). The integration of such resources has shown a substantial impact on DN through power loss reduction and improved network reliability. Besides this, the existing protection system has encountered coordination challenges due to the bidirectional power flow, different types and capacity of generation sources, and changes in fault levels due to network operating modes (grid-connected or islanded). Such conditions may cause the relays to malfunction and imperil the effectiveness of the existing protection scheme. Therefore, an efficient and robust protection coordination scheme is imperative to avoid network reliability and stability issues to the grid. This review paper presents a comparative analysis of various protection techniques implemented to alleviate the impact of integrated resources into DN. Moreover, a comparison of classical and modified protection approaches in terms of advantages, shortcomings, and implementation costs is presented. The prime objective of this study is to highlight the prominence of utilizing user-defined programmable relays for modern DNs. Moreover, recommendations are presented by considering the application of user-defined relay characteristics that can be proved as a robust protection scheme to cope with the protection challenges in existing and future power systems developments.

There is a growing interest in increasing the penetration rate of renewable energy systems due to the drawbacks associated with the use of fossil fuels. However, the grid integration of renewable energy systems represents many challenging tasks for system operation, stability, reliability, and power quality. Small hybrid renewable energy systems (HRES) are small-scale power systems consisting of energy sources and storage units to manage and optimize energy production and consumption. Appropriate real-time monitoring of HRES plays an essential role in providing accurate information to enable the system operator to evaluate the overall performance and identify any abnormal conditions. This work proposes an internet of things (IoT) based architecture for HRES, consisting of a wind turbine, a photovoltaic system, a battery storage system, and a diesel generator. The proposed architecture is divided into four layers: namely power, data acquisition, communication network, and application layers. Due to various communication technologies and the missing of a standard communication model for HRES, this work, also, defines communication models for HRES based on the IEC 61850 standard. The monitoring parameters are classified into different categories, including electrical, status, and environmental information. The network modeling and simulation of a university campus is considered as a case study, and critical parameters, such as network topology, link capacity, and latency, are investigated and discussed.

The deployment of smart grids has facilitated the integration of a variety of investor assets into power distribution systems, giving rise to the consequent necessity for positive and active interaction between those investors and local distribution companies (LDCs). This paper proposes a novel incentive-based distribution system expansion planning model that enables an LDC and distributed generation (DG) investors to work in a collaborative way for their mutual benefit. Using the proposed model, the LDC would establish a bus-wise incentive program based on long-term contracts, which would encourage DG investors to integrate their projects at specific system buses that would benefit both parties. The model guarantees that the LDC will incur minimum expansion and operation costs while concurrently ensuring the feasibility of DG investors' projects. To derive appropriate incentives for each project, the model enforces several economic metrics including internal rate of return, profit investment ratio, and discounted payback period. All investment plans committed to by the LDC and the DG investors for the full extent of the planning period are then coordinated accordingly. Several linearization approaches are applied to convert the proposed model into an MILP model. The intermittent nature of both system demand and wind- and PV-based DG output power is handled probabilistically, and a number of DG technologies are taken into account. Case study results have demonstrated the value of the proposed model.

Nowadays, neural networks (NN) are being utilized in different control problems because of their excellent ability to model any nonlinear process. NN is suitable for the process having a wide range of operating conditions. In this work, the neural network-based internal model control (NN-IMC) scheme has been considered as a secondary controller for the load frequency control (LFC) problem in the restructured electricity market in order to meet Poolco and bilateral transactions. The proposed control scheme has been implemented on a 75-bus, 15-generator power system. The test system is divided into four areas. It is seen that area frequency errors have been eliminated at a steady state in all cases, and Gencos/Discos shared the increase in demand as per their involvement in the frequency regulation market. The results show that the NN-IMC control scheme has good performance and improves system responses effectively. Further, the performance of the NN-IMC control scheme has also been compared with the fractional-order proportional-integral-derivative (FO-PID) control scheme It is observed that the performance of the FO-PID controller is superior to the NN-IMC scheme in terms of settling time and similar to the NN-IMC control scheme in terms of maximum overshoots/undershoots. The compliance of the NN-IMC scheme has also been checked with NERC standards. It is seen that the NN-IMC scheme also satisfied the CPS1 and CPS2 control standards.

Reliable estimation of solar irradiance is required for many solar energy applications such as photovoltaics, water heating, cooking, solar microgrids, etc. Deep Learning techniques have shown outstanding behaviour for analysing complex datasets efficiently with high accuracy. Multi-Layer Perceptron, Long-Short Term Memory, and Gated Recurrent Unit techniques are found to be the most competitive techniques in the literature for solar irradiance forecasting. Therefore, in this study, a comparative analysis of those models is carried out using eleven years of NASA satellite data for training and testing. The grid search technique is used to optimize the networks architectures to ensure the best performance of the models for forecasting daily global solar irradiance. The results show that all models have almost the same accuracy with a mean square error close to 0.017 kWh/m2/day. However, the speed of training and the complexity differ for each model. The MLP is found to be the most efficient model due to using a low number of parameters. The study is of importance for reliable solar irradiance forecasting for any location worldwide.

The sizing problem of the hybrid energy system (HES) is a crucial issue especially in rural communities because any wrong results can mislead the decision makers for building the new HES. Due to the intermittent nature of the renewable energy sources (RES) such as wind and PV, there will be a need for a high storage system and/or a standby diesel engine, which increase the investment, required, and increases the cost of energy (CoE). The use of smart grid concepts like the demand response (DR) using dynamic tariff can improve the system performance, enhance the stability, reduces the size and investments of HES components, reduces the customers' bills, and increases the energy providers' profits. The DR strategy will allow the customers to share the responsibility of the HES stability with the energy providers to maintain the stability of the HES. The DR strategies should be selected to ensure the balance between the available RES and the load requirements. In this article, a novel DR strategy is introduced to model the required change in the tariff with the battery state of charge and its charging/discharging power. The novel DR strategy is used in the sizing of the HES based on techno-economic objectives using three different soft computing optimization techniques. This article introduces modeling and simulation of the smart grid integrated with hybrid energy systems to supply a standalone load for a rural site in the north of Saudi Arabia. The sizing of the HES is built based on minimizing the CoE and the loss of load probability. The novel DR strategy introduced in this article reduced the size of the HES compared to the fixed load technique by 20.66%. The results obtained from this novel strategy proved its superiority in the sizing and operation stage of the HES.