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— This paper introduces a comprehensive modeling approach for optimizing the capacities of various energy converters and storage systems within a residential Energy Hub (EH). The model focuses on minimizing energy supply costs to customers while maximizing the utilization of renewable and hydrogen-based technologies. Through the optimization process, we determined that implementing CHP units in the EH could reduce total costs by approximately 78% with green tax incentives and by 46.7% without them. Additionally, the inclusion of an incentive to the EH resulted in a 27.2% reduction in total costs imposed on subscribers. The study also evaluates the impact of uncertainties in photovoltaic (PV) electricity generation, demonstrating the benefits of using stochastic optimization over deterministic methods. The results indicate that the economic advantage of this approach over deterministic methods is 86.75%–95.74% greater, depending on the scenario. Sensitivity analysis further reveals that the investment costs of fuel cells significantly influence the optimal capacity design and economic viability of the EH model. • Proposing a modeling procedure for hydrogen within renewable-based energy hub studies. • Extracting a probabilistic model for solar radiation and ambient temperature. • Presenting various operating strategies in planning studies of residential energy hubs. • Developing a stochastic optimization algorithm for running the proposed model. [ABSTRACT FROM AUTHOR]

To fully tap the abilities of renewables in reactive power optimization, this paper develops a detailed model for the power regulation capabilities of wind turbines and photovoltaic units and studies their impact on the power system’s operation. First, the power system model with renewables integration is established using AC power flow. The wind turbines and photovoltaic units are modeled in detail according to their topologies and operating characteristics, and then further simplified according to the feasible region. An improved DC power flow model is adopted to handle the non-linear characteristics of the power system. On this basis, a multi-objective reactive power optimization model is constructed to minimize the power generation cost, wind and solar power curtailment, and voltage offset. Finally, comparisons between two types of models in different scenarios are designed. Numerical simulations demonstrate that the participation of renewables in reactive power regulation can improve the operational economy and voltage stability of power systems.

This paper presents a model for line extension scheduled to participate in responsive loads in the power system aiming the improvement of techno-economical parameters. The model is studied with the presence of photovoltaic generators that produce variable power depending on the geographical condition. The investment cost of the transmission expansion plan, demand response operation cost, generation costs and the sum of the voltage deviations are the four indices that the optimization problem is designed based on these four criteria. Objective functions are dynamic variables that change daily due to variation in generation and load. A multi-objective optimization method based on the analytic hierarchy technique is employed to solve the problem. The Pareto-optimal set is extracted with gravitational search style and the best solution is fund by AHT manner. Studies are carried out on the modified 30-bus and 24-bus IEEE test system to confirm the capability of the presented model. Two frameworks are defined to compare the suggested manner. A different amount of PV penetration is discussed in several scenarios. Also, load uncertainty is formulated and involved based on probability distribution function.

Grid‐interfaced solar photovoltaic (GIPV) system requires a robust control algorithm for satisfactory operation under grid voltage disturbances. In this study, an application of Newton's learning rule‐based total least‐square estimation employing single‐layer neural network structure is adopted to interface the photovoltaic unit with the utility grid. In addition, the weight‐updating mechanism is uniquely integrated with threshold neuron for attaining speedy extraction of fundamental load current component. In particular, the proposed control algorithm has the capability to transfer maximum active power to the utility grid/AC load at unity power factor while operating as distribution static compensator to offer various ancillary services including current harmonics attenuation and reactive current suppression. This will lead to increased device utilisation factor of the overall GIPV system during night time (i.e. in the absence of solar irradiance). Moreover, the proposed control scheme is designed and developed without any complex transformations and derivative terms, which results in less computational intensiveness. The GIPV system with three‐phase double‐stage configuration is modelled in MATLAB/Simulink software using sim‐power system tool. Finally, the adaptability of the proposed control algorithm has been verified and confirmed through 500 W laboratory prototype using low‐cost Arm Cortex‐M4 microcontroller under various operating conditions. [ABSTRACT FROM AUTHOR]

This study proposes a comprehensive framework for developing a multi-energy off-grid microgrid with the decoupled flow of thermal and electrical energies in a rural setting. A carbon-neutral microgrid with a hybrid generation system constituting a photovoltaic unit and a biofuel generator is proposed. In order to enhance the fuel utilisation efficiency, the biofuel generator is operated in combined cooling, heating, and power mode, and the recovered thermal energy forms the heat distribution network in the microgrid. The flexibility of system operation is improved by suitable multi-energy (electrical and thermal) storage. Firstly, an optimal sizing framework has been developed for the system as a mixed integer linear programming model. Secondly, a coordinated multi-energy management system (MEMS) has been developed for combined optimal dispatch of multiple generation and storage resources. The MEMS has been developed as a mixed integer non-linear programming model, which minimises system operational cost while considering minimum battery degradation to prolong its lifetime. Finally, a detailed economic analysis of the proposed system has been presented, highlighting the levellised cost of energy and net present value. Extensive case studies and simulation results depict the effectiveness and suitability of the proposed MEMS for the rural off-grid microgrid.

One of the most crucial and economically-beneficial tasks for energy customers is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may vary. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data-driven forecasting method based on fuzzy logic (FL) for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, namely left and right from load peak, for the sake of searching for equal BESS capacity. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) considering BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak power shaving/curtailment method, offline digital time-domain simulations have been performed on a test microgrid system using the data gathered from a real-life practical test microgrid system in the MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical microgrid system with real-life data obtained from smart meters and also, compared with several previously-reported methods.

In this paper, a stochastic model is proposed for coordinated scheduling of combined heat and power units in micro grid considering wind turbine and photovoltaic units. Uncertainties of electrical market price; the speed of wind and solar radiation are considered using a scenario-based method. In the method, scenarios are generated using roulette wheel mechanism based on probability distribution functions of input random variables. Using this method, the probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective function, coordinated scheduling of combined heat and power, wind turbine, and photovoltaic units change this problem to a mixed integer nonlinear one. Therefore to solve this problem modified particle swarm optimization algorithm is employed. The mentioned uncertainties lead to an increase in profit. Moreover, the optimal coordinated scheduling of renewable energy resources and thermal units in micro grids increase the total profit. In order to evaluate the performance of the proposed method, its performance is executed on modified 33 bus distributed system as a micro grid. [ABSTRACT FROM AUTHOR]

This paper makes two contributions. Firstly, it is shown that there may exist oscillations in distribution networks with physically close photovoltaic (PV) units due to their control interactions. The main reason for oscillations is changes in the dynamics of PV units with varying solar irradiance and the decentralized tuning of control parameters for the nominal operating condition. Eigenvalue analysis, nonlinear interaction index, and time-domain simulations are used to assess the degree and impacts of dynamic interactions. Secondly, a decentralized robust control design is proposed to ensure a non-interacting and well-damped response under varying operating conditions for physically close PV units. The control design process uses an estimate of changes in the model due to variations in solar irradiations, changing electric loads, and the dynamics of the physically close PV units. A minimally conservative method is used to capture the estimate as a norm-bounded uncertainty. Time-domain simulations on a test distribution system verify the predictions of the interaction analysis and demonstrate the performance of the proposed robust controller under different system contingencies. [ABSTRACT FROM AUTHOR]

One of the most crucial and economically-beneficial tasks for energy customers is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may vary. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data-driven forecasting method based on fuzzy logic (FL) for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, namely left and right from load peak, for the sake of searching for equal BESS capacity. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) considering BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak power shaving/curtailment method, offline digital time-domain simulations have been performed on a test microgrid system using the data gathered from a real-life practical test microgrid system in the MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical microgrid system with real-life data obtained from smart meters and also, compared with several previously-reported methods. [ABSTRACT FROM AUTHOR]