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The single-stage T-type three-port converter enables efficient and accurate power distribution between two dc ports and one ac port without embedding an auxiliary dc/dc converter. Considering the diversity of loads, this article adds a T-type fourth leg to efficiently handle asymmetric and nonlinear loads. Accordingly, a new simplified space vector modulation strategy suitable for the asymmetric vector space is proposed. Only eight special switching states are used to compose the reference vector. Then, a unified algorithm is presented to obtain the maximum power distribution range of the dc port for both the T-type three-leg and four-leg three-port converters. Besides, to reduce the limitation on the dc port power transfer from the light loads, a novel technique based on the zero-sequence current is proposed to significantly expand the power distribution range. This method leverages the zero-sequence loop provided by the basic filtering components to generate additional converter currents that are crucial for the power distribution. The effectiveness of the newly proposed strategies is convincingly demonstrated through tests conducted on a 3-kW prototype.

This paper presents a day-ahead forecasting method for photovoltaic (PV) power plants in commercial sectors. The method is based on numerical weather prediction (NWP) models from open weather maps and power plant specifications. The output of the model is the predicted power output from the PV power plant, which is incorporated into an optimal control strategy of the PV plant using battery storage. The use of optimal algorithms assists in the PV power plant curtailment in cases of over-generation and reduces the dependence on conventional power sources such as generators in cases of under-generation by the PV plant. It was found that most forecasting methods do not incorporate PV power and storage systems for proper optimization and demand management. This can be seen as a gap for further research of forecasting models integrated with battery storage systems to improve PV power system performance. Results obtained show a good performance of the developed model. A root means square error (RMSE) of 425.79 W and 595.10 W and a mean absolute error (MAE) of 246.26 W and 238 W were achieved for a summer and winter day, respectively. Furthermore, an excellent positive correlation exists between the predicted output power and the observed results, with R2values over 90%.

Fossil fuels are rapidly running out, and with the demand for environmentally friendly energy sources increasing, power grids are looking for distributed power generation-based renewable resources. The distribution of these energy sources is significantly linked to the development of smart microgrids, which are also extensively connected with the energy internet. This paper explores the energy internet operation, focusing on developing a routing algorithm for an energy router. The energy routing algorithm is further substantiated with the aid of simulations. This algorithm can find all the paths available for energy transmission between two nodes and selects the track with the most negligible losses as the path for transmission. All the possible routes are displayed along with the losses associated with each direction to ensure that the approach with the lowest losses is taken. The algorithm is also tested for 24 h at an hourly interval to observe the change in power transmitted on the system.

This study presents a strategy to optimize hybrid power system dispatch for commercial sectors in South Africa while utilizing the day-ahead method to forecast solar photovoltaic (PV) power. The approach utilizes numerical weather prediction (NWP) models obtained from open weather maps and incorporates power plant specifications to generate predictions of the PV power plant’s output. These predictions are then integrated into an optimal control strategy incorporating battery storage. The use of optimal algorithms helps manage PV power plant curtailment during periods of over-generation. It is crucial to optimize PV power systems and ensure a continuous power supply for solar power plants, even during unfavorable weather conditions. Besides, the study develops a model that solves the challenging questions of combining solar power forecasting with an optimal dispatch and demand management scheme. Therefore, there is a need to incorporate battery storage systems through the developed optimal control method to maximize the energy from the PV system and minimize the power from the utility grid. The obtained results demonstrate the effectiveness of the developed model. The winter season presented a lower MAE of 21 kW, an RMSE of 35.4 kW, and a MAPE of 3,1% for PV power output forecasting, showing that the errors during prediction are lower compared to other seasons. It has been observed that 60% of the load is supplied through a combination of PV power and battery storage. Therefore, evidence of the developed optimal hybrid power dispatch with an innovative solar power forecasting model suggests that accurate forecasting can improve system planning and mitigate the necessity of procuring grid power at high electricity prices.

This article offers an islanding detection approach based on dual-tree complex discrete wavelet transform (DTCDWT) and extreme gradient boosting method (XGBoost). Initially, the DTCDWT is used to extract the feature vector from collected negative sequence voltage and current signals at the respective distributed energy resource ends. Afterward, the nonlinear synthetic minority oversampling technique (SMOTE)-based XGBoost is used to classify the islanding (I) and nonislanding (NI) events with the following characteristics: high accuracy, good sensitivity, and faster speed. Here, the SMOTE is used as an offline prefeature processing approach to reduce the unbalancing/uncertainty of “I” and “NI” classes and helps the intelligent classifier to impart a fair classification result. This scheme is found to be effective for the detection of I and NI conditions, satisfying the IEEE islanding detection standard. Additionally, the obtained simulation results are compared with other existing works to show its usefulness.

A typical 2 × 25 kV traction power supply system consists of both primary and secondary equipment housed in control rooms, and the traction overhead wiring (OHW) that runs between the substations. The substation secondary equipment consists of the protection and control intelligent electronic devices (IEDs) known as relays. Each section of the OHW between the substations is protected by a set of two relays at both ends of a line for impedance protection and their backup relays. At substations facing the sections at the end of the line, where there is no other substation on the other end, usually the overcurrent and earth fault protection is used. At the substations, there are disturbance fault recorders installed or relays equipped with a capability to record and store disturbance fault records. An analysis of these fault records makes a significant contribution to the efficient running of a traction system such as the Gautrain rapid rail system. A combination of the analysis of the faults that occur in the OHW as recorded and stored in the relays as well as the performance of the IEDs and the protection settings using the on-site tests are a subject of this article.

Several features of innovative grid technologies can be deployed for the overall performance of the power system environment. This can be seen from the generation to the consumption of energy. The two-way communication of smart metering introduces the novel functionalities of the energy management system. This paper presents a practical implementation of using the intelligent metering system. It consists of implementing a nanogrid that optimally coordinates the energy from the solar panel, battery storage and utility grid to supply the end user. The developed model is validated with an optimal value of the state of charge of the distributed energy storage to maximise energy from the solar panel and battery storage while minimising the power received from the utility grid. A demand response scheme is employed to formulate the performance index of the energy management system using three optimal control models: adaptive open-loop control, adaptive closed-loop Control and model predictive control schemes. The formulation of the performance index of each approach is a function of the energy flow from different resources depending on the power consumption. The three models have given different insights into the performance of the smart nanogrid, which may be used to the Advantage of the grid owner or end user. Through the performance of the optimal strategies, it can be observed that energy management is ensured, and real-time monitoring of the entire system is guaranteed. The performance models facilitate the minimisation of the power from the utility, resulting in savings between 23.7 % and 39.240% of the total energy demand from the end user. Besides, the system design is validated by an electrical system to form a real-world innovative nanogrid application in residential environments.

With affordable and clean energy being one of the Sustainable Development Goals (SDG7), most developing economies are still caught up with the dilemma of inadequate power supply and heavy dependence on fossil fuel. This social menace is premised on rapid population growth, industrialization, modernization, etc. Even though these sources of power appear to be far-fetched from being sufficient, they are noted for creating a significant level of environmental pollution, global warming, and health-related risks. The Conference of Parties 26 (COP26) assembly held in Glasgow, United Kingdom, stressed the need to bring down the rising annual global temperatures to 1.5°, with developing economies having a significant role in achieving this target. This article has presented a review with insight into certain power generation metrics within the context of (SDG7). This span across the investigation of different energy modelling tools, their depth of effectiveness, the general pros and cons of energy policies premised on these tools, and progress made so far towards the development of an affordable and clean power sources mix in developing economies. A deduction was reached that there is an immense potential for power generation from affordable and clean energy sources as this bridges the enormous gap between power demand and supply as well as mitigates greenhouse gases (GHGs) effects.

•Present an overview of the grid-connected sustainable energy system for the urban sector.•Evaluate worldwide energy scenarios and the adding value of renewable energy resources.•Analyse different opportunities of implementing distributed energy resources, PV system and energy storage system, for an optimal dynamic behaviour scheme.•Detail the future perspective of using distributed energy resources connected to the utility grid based on the smart grid environment.