HYBRID SOLAR-WIND INTEGRATION USING AN ADAPTIVE NETWORK RECONFIGURATION METHOD AND CONTROLLING FOR UNCERTAINTY-AWARE SMART GRID BY OPTIMIZATION ALGORITHM

The aim of this paper is to manage the power flow in hybrid renewable energy sources in efficient manner. In this there will be two renewable energy sources, battery, load and grid. In this system the bidirectional buck boost converter is used for PV source and battery, dual half bridge converter with transformer coupling and inverter for load/grid. In a standalone system, PV and wind source are made to operate in Maximum Power Point mode where the load will take the required power. In case of systems which are connected to the grid, two sources will always be operating at its MPP. If both the sources are not present, the required power will be taken from the grid and is used for charging the battery when the situation arises. MATLAB/Simulink is used to obtain the simulation results and the performance for the management of power flow is determined for various modes of operation

This paper proposes a multistage distribution network planning (DNP) method that considers multiple planning alternatives, the active management of distributed generation (DG) units and demand response in the context of active distribution networks (ADNs). Representative days are selected for the consideration of uncertainties related with load demand and renewable power generation, during the planning period. The proposed DNP method aims at minimising the net present value of the total investment and operational cost. The proposed DNP considers multiple planning alternatives, such as the reinforcement of existing substations and existing distribution lines, the installation of new distribution lines, and the installation of capacitors. Furthermore, the proposed DNP method incorporates the control capabilities of DG active and reactive power output and demand response schemes. A 24‐bus distribution network and a 144‐bus real world distribution network are used to validate the performance of the proposed method.

With the increasing penetration level of renewable energy in the integrated energy system (IES), water electrolysis for hydrogen production (WEHP) technology has become an effective solution to increase the capacity of renewable energy utilization. Therefore, this paper proposes to take WEHP and hydrogen fuel cell (HFC) as flexible resources to participate in the optimal scheduling of IES under grid-connected/off-grid operation mode. Firstly, focusing on the production, storage and utilization of hydrogen energy, a unified operation model of WEHP module is built by analyzing the differences of the three WEHP technologies. And then, “co-generation” model of HFC, the residual capacity and transportation cost model of hydrogen storage device (HSD) are built. Second, by analyzing the uncertainty of light intensity and wind speed, a prediction error correction method is proposed to improve the prediction data accuracy of renewable energy power generation. Third, taking the lowest daily operation cost as the objective function, the optimal scheduling model of electricity-heat-hydrogen integrated energy system under grid-connected/off-grid operation mode is constructed. On this basis, the operation scheduling model of electricity-heat-hydrogen integrated energy system is transformed into a mixed integer linear model by piecewise linearization method, and the model is solved by CPLEX solver. Finally, an example is given to verify the rationality of the proposed scheduling model in the grid-connected/off-grid operation mode. On one hand, reasonable optimal scheduling of multiple WEHP combination is helpful to reduce the daily operation cost of IES. On the other hand, by correcting the prediction data deviation of renewable energy power generation, the equipment output of electricity-heat-hydrogen integrated energy system can be more reasonable, which is helpful to reduce the daily operation cost and improve the operation reliability. The effects of sensitive factors such as the penetration level of renewable energy, the relative fluctuation of electricity price and hydrogen price on the operation cost of optimal scheduling scheme are further analyzed, which provides a theoretical reference for the optimal scheduling of IES.

The worldwide demand for reduction of CO2 pollution, with more penetration of renewable energy sources and an increased number of electric vehicles (EVs), demonstrates the importance of economic dispatch (ED) with taking into account the reduction of CO2 emission. ED is a classical problem in which EVs impose more penetration as a dynamic load, and its impact as vehicle-to-grid (V2G) is the possible future trend with cost minimization. Based on the integration of EVs and hybrid renewable sources concerning both economic dispatch and pollution minimization, the multi-objective function is converted into a single comprehensive objective by using the judgment matrix methodology. In this paper, the investigation involves the minimization of the cost of all three objectives viz. operation cost, pollution cost, and carbon emissions with ED by incorporating V2G technology. The algorithms which include particle swarm optimization, as well as artificial bee colony, are applied under various operation and control strategies. The proposed models are verified and analyzed with different case studies. In terms of operation economics, the simulation results validate the superior performance of EVs based microgrid (MG) model in the coordinated charging and discharging mode. Further, the comparison of both algorithms shows better results with the ABC algorithm in terms of cost minimization of all objectives. ABC is better in V2G based microgrid with coordinated charging and discharging mode while its performance is significant during a large number of EVs (i.e., 700 EVs). Moreover, the load shedding scenarios are integrated which enables the MG system to operate in dual mode (i.e., seamless transition). In this paper, the main contribution involves penetration of EVs as dynamic load and its V2G impact in a coordinated or uncoordinated way, application of ABC algorithm for this particular load problem with improved results, and inclusion of short-term load shedding scenarios.

Unlike the traditional way of efficiency assessment of renewable energy sources integration, the smart grid concept is introducing new goals and objectives regarding increased use of renewable electricity sources, grid security, energy conservation, energy efficiency, and deregulated energy market. Possible benefits brought by renewable sources integration are evaluated by the degree of the approach to the ideal smart grid. In this paper, fuzzy analytical hierarchy process methodology for the integration efficiency has been proposed, taking into account the presence of multiple criteria of both qualitative and quantitative nature, different performance indicators, and the uncertain environment of the smart grid. The methodology has been illustrated on the choice of the size and location of a distributed generator in the radial distribution feeder.

The global energy landscape confronts unprecedented challenges in generation expansion planning, characterized by increasing renewable energy integration, complex technological uncertainties, and the critical need to simultaneously optimize economic, environmental, and reliability objectives. Traditional deterministic planning approaches have proven inadequate for capturing the dynamic and stochastic nature of modern power systems. This research aims to develop a comprehensive multi-objective generation expansion planning framework capable of effectively modeling renewable energy integration, quantifying deep uncertainties, and providing robust decision support mechanisms for strategic infrastructure planning. The study employs a hybrid stochastic-robust optimization approach, integrating advanced techniques including Monte Carlo scenario generation, econometric load forecasting models, spatial-temporal correlation modeling, multi-criteria decision analysis, and sophisticated mathematical optimization algorithms. The proposed framework generated 147 non-dominated Pareto solutions, demonstrating superior performance compared to traditional models. Key achievements include revealing non-linear cost-emission trade-offs, identifying critical renewable integration thresholds, achieving a hypervolume indicator of 0.847, and comprehensive validation through an IEEE 118-bus test system. Ultimately, the research presents a transformative approach to generation expansion planning, offering a sophisticated methodology that bridges technological complexity, uncertainty management, and strategic decision-making in the evolving global energy landscape.

The author proposes an effective capacitor control model for unbalanced radial and meshed distribution systems. Due to the linear characteristic of the proposed model, a constant sensitivity matrix relating the incremental capacitor shifts and system status can be derived, and then the sensitivity-based objective function and network constraints can be obtained. The linear formulation can be solved by the commonly used linear programming and integer programming techniques, which are among the best choices for real-time control in terms of computational speed, reliability, and ability to handle many different operating constraints. The development of the sensitivity matrix does not need any assumptions about voltage magnitudes, voltage angles, line r/x ratios, and network topology; thus, the proposed method can achieve high robustness and accuracy. The proposed capacitor control model can be used to solve the capacitor placement and real-time capacitor control problems; however, in order to verify the accuracy of the model, only the corrective dispatching problem is solved in this work. Test cases including the unbalanced radial and meshed distribution systems and a large-scale distribution system acquired from Taiwan Power Company are all conducted. Test results show that the proposed method can effectively handle the capacitor control problems and has great potential to be integrated into distribution automation.

The paper presents an analysis of voltage dynamics for a test system, considering or not renewable sources integration in the electricity grid. The analysis was carried out for the n-1 contingency criterion. The simulations were conducted using PowerFactory DigSilent software and the New England 39-bus test system.

Life cycle implication of the potential commercialization of stover-based E85 in China

MicroGrids are power generation and distribution systems in which users and generators are in close proximity, They usually have limited power generation capacity, and are networked together to meet a small area's load demand. MicroGrid, (MG) car operate interconnected to the main power network or be operated autonomously, if they are isolated from the power grid (islanded mode). The interconnection of large amounts of nontraditional generation causes problems in a network designed for conventional operation. The use of power electronics interfaces offers a potential solution. This paper presents a generalized formulation to determine the optimal operating strategy and cost optimization scheme for an electronically-coupled MicroGrid. The major objective is to minimize the overall operating cost considering both the p ower system and power electronics constraints in the two modes of the MG operation. The paper also presents steady-state, fundamental-frequency models of power electronic converters systems for coupling distributed generation (DG) units to the utility power grid based on Newton-Raplison and the developed models. A feature of the proposed approach is that it solves for the internal variables of each DG unit. A Matlab program is developed to represent the proposed algorithm. The program is tested in various network conditions and verified by applying it to a MG with three DG units from which two units are electronically-coupled to the grid in the two modes of operations.

The paper presents the impact of connecting Renewable sources to the Smart Grid with regard to improving Power Quality aspects. The impact of integrating renewable sources on Power Quality indices in the grid wes analyzed and the measures for avoiding the negative impact are included in the paper. Challenges coming from the integration of renewables – variability and uncertainty and the correlation with smart grid technologies used in the power system are discussed in detail. Aspects related to the exchange of information among different systems used in the Smart Grid–DMS, SCADA, DFR, OMS, MDMS, security of the grid and security of IT networks have been reached in the work. The final part of the paper contains the authors conclusions on issues addressed in it.

In this paper, impacts of renewable sources on the adequacy of a 2 area system are presented. Integration of popular renewable sources like photovoltaic (PV) and wind is forcing the operational planner to operate differently. Here, an effort has been made to propose some planning scenarios for 2 area system on the basis of renewable sources integration, and reliability is evaluated corresponding to each scenario. Loss of load expectation is popular method for adequacy assessment following 2 state principle (available or unavailable) of conventional sources. Photovoltaic and wind system never follow 2 state principle unlike conventional. Existing PV module configuration determine the unavailability rate of PV plant considering component failure. Random wind speed is simulated using time series auto regressive moving average model. Wind turbine characteristics and simulated wind speed create multistate wind energy conversion system and reduced by apportioning method to predict unavailability rate. A simple 2 area system is considered to validate the efficacy of proposed theme.

In this paper, a gas turbine-based distributed energy system (DES) model is developed for the design of operation planning. An operation mode aimed to optimize the operation of this DES is proposed. A multi-objective cost function considering the total system efficiency and operational cost is formulated for the optimal design of DES operation and control. A two-stage approach combining the particle swarm algorithm (PSO) with the sequential quadratic programming (SQP) method is employed to solve the nonlinear programming problem. Optimal operation strategies for the DES are investigated using the proposed two-stage method under three different demand loads in terms of weather conditions. The simulation results are compared with those using traditional rule-based operation methods. It is found that under the proposed operation mode, the DES is capable of achieving an improved performance in terms of thermal efficiency and operational cost.

Integrating more renewable electricity into the power system may increase carbon emissions

Massive inclusions of Renewable energy generation on the grid, increases uncertainty in generation schedule decision. As a result, traditional reliability index such as Lost of Load Probability (LOLP) may not be reliable criteria to evaluate system reliability. Thus, a revised formulation of unit commitment (UC) is required, which not only optimize day-ahead generation schedule for system with high penetration of renewable energy, but also stay committed to the security of the system as well. In this paper, our novel approach of a revised UC formulation with system security considerations and dynamic spinning reserve (SR) is presented. With considerations of uncertainties in both demand and renewable power generation, together with random outages of generators, an analytic algorithm is presented to calculate the optimal required spinning reserved for every time interval and the reliability cost. Our approach considers the reliability of system grid and the hourly forecast of variable demand and renewable generation sources such as wind and photovoltaic, together with their uncertainties. Particle Swarm Optimization (PSO) is utilized to solve the revised UC formulation. Numerical tests are performed and the results are analyzed for an IEEE RTS bus system. Simulation results on certain 4-unit case system validates the efficacy of our methodology.