Reactive Power Resources Research Articles

СКЛАДОВІ ПОТУЖНОСТІ ТИРИСТОРНОГО РЕГУЛЯТОРА НАПРУГИ З АКТИВНИМ НАВАНТАЖЕННЯМ

A study of the power balance of the semiconductor voltage regulator on an active load with phase control by two-operation thyristors was carried out. The suitability of its use for simultaneous obtaining of the reactive power resource for distribution electric networks has been confirmed. According to the concept of intelligent electrical networks, such devices belong to the class of flexible alternating current transmission systems, and they can be the basis for building virtual energy sources. The power components for individual components of the regulator, including the thyristor block, were studied. The spectral method of describing currents and voltages at the regulator sections is applied. The obtained results of theoretical studies were confirmed by an example with numerical calculations, which made it possible to clarify the physical essence of energy transformations during voltage regulation. Bibl. 8, fig. 3.

Coordinated control strategy of photovoltaic energy storage power station based on adaptive dynamic programming

In order to solve the problem of variable steady-state operation nodes and poor coordination control effect in photovoltaic energy storage plants, the coordination control strategy of photovoltaic energy storage plants based on ADP is studied. Establish the photovoltaic energy storage power station model including photovoltaic system model, super capacitor system model and battery system model; Set the maximum limit of active power change as the power constraint condition for coordinated control of photovoltaic energy storage station; The optimal control problem of multi voltage and reactive power resource coordination is fully considered, the optimal voltage control model is established by using ADP algorithm, and the optimal coordinated control strategy is obtained by online learning the collected dynamic operation information. The experimental results show that this strategy can improve the coordinated control effect of the photovoltaic energy storage station, ensure the photovoltaic energy storage station in a stable operation state, improve the service life of the energy storage device in the photovoltaic energy storage station, and stabilize the steady-state operating node voltage within the ideal fluctuation range.

Utilize the Prediction Results from the Neural Network Gate Recurrent Unit (GRU) Model to Optimize Reactive Power Usage in High-Rise Buildings

The growing urbanization and the construction sector, efficient use of electric energy becomes important, especially the use of reactive power. If excessive use causes decreased efficiency and increased operational costs. Decreased efficiency contributes to increasing exhaust gas volumes and greenhouse emissions. Efficient energy can achieved if planning and predictions are correct. This research applies the GRU neural network method with grid search initialization as a novelty predictive model for energy-use high-rise buildings in form fast training without multiple iterations because optimal hyperparameters are obtained. Experimental show the MAE and RMSE performance metrics of the GRU better than LSTM in predicting energy consumption data peak loads, off-peak loads and reactive power. The accuracy of GRU predictions can optimize the use of energy to contribute to saving the environment from exhaust emissions and the greenhouse effect in urban systems. Experimental results demonstrate the superiority of GRU over LSTM, proof of the much lower MAE and RMSE values. This metric shows the accuracy of GRU in generalizing data both during peak and off-peak hours, as well as in reactive power usage. By Utilizing GRU's capabilities, building management can manage reactive power usage effectively, allocate reactive power resources appropriately, and mitigate peak load times and the power factor within the threshold, thus avoiding additional costs and electrical system efficiency and contributing to reducing the carbon footprint and gas emissions greenhouse. Research on GRU is widely open in the high-rise building sector, including its integration with sensors to automatically control energy use.

Series and shunt FACTS controllers based optimal reactive power dispatch

Optimal reactive power dispatch involves the determination and management of reactive power resources in a power system to maintain voltage stability, improve power transfer capability, and minimize system losses. Reactive power is essential for maintaining voltage levels within acceptable limits and ensuring the reliable operation of electrical networks. The whale optimization algorithm (WOA) has been proposed to obtain the optimal location of flexible alternating current transmission system (FACTS) components. The efficacy of WOA is tested using conventional IEEE 14 and 30 bus test systems. Static var compensator (SVC) is used as shunt and the thyristor-controlled series capacitor (TCSC) as a series FACTS controller. The analysis is carried out for both the systems with and without FACTS controllers. Optimization techniques are applied to select the optimal control parameters. The suggested strategy is compared to other contemporary techniques such as particle swarm optimization (PSO) and grey wolf optimization (GWO). At various loading situations, the WOA-based technique outperforms other two techniques.

Application of fully automated centralized voltage regulation in transmission system operation management

The influence of wind farms, inclusion of distributed energy and the energy market has a large impact on the voltage profile in a transmission system. The Croatian Transmission System Operator HOPS has implemented VVC (Volt Var Control) system which was one of the subprojects included in the EU co-founded Sincro.Grid smart grid project that was financed by the EU CEF fund. The main goal of the project is to raise voltage quality in the transmission network. VVC system is an optimal power flow-based application which calculates the optimal solution regarding the desired objective function, available control variables and a defined set of constraints. To achieve the calculated optimal power system state, control of field devices is included in the process of optimization by shifting tap changer positions or changing setpoint values of reactive power injection. Voltage and reactive power constraints are set accordingly to available regulating devices included in the optimization.
 The lack of automatic coordination of reactive power resources motivated this work for implementing an advanced VVC for real time control of reactive power in partially automatic and fully automatic (closed loop) mode using the optimal power flow (OPF) algorithm. Due to its complexity, the closed loop approach of reactive power regulation is rarely used in the TSO community and the HOPS is one of the first TSOs that implemented regulation in such manner. Except fully automatic mode of operation, VVC can be used in semi-auto or manual mode. For a successful implementation several safety algorithms are implement to avoid many unwanted situations in the power system such as voltage breakdown, overloading of regulating equipment and similar

Two-stage Stochastic Programming for Coordinated Operation of Distributed Energy Resources in Unbalanced Active Distribution Networks with Diverse Correlated Uncertainties

This paper proposes a stochastic programming (SP) method for coordinated operation of distributed energy resources (DERs) in the unbalanced active distribution network (ADN) with diverse correlated uncertainties. First, the three-phase branch flow is modeled to characterize the unbalanced nature of the ADN, schedule DER for three phases, and derive a realistic DER allocation. Then, both active and reactive power resources are co-optimized for voltage regulation and power loss reduction. Second, the battery degradation is considered to model the aging cost for each charging or discharging event, leading to a more realistic cost estimation. Further, copula-based uncertainty modeling is applied to capture the correlations between renewable generation and power loads, and the two-stage SP method is then used to get final solutions. Finally, numerical case studies are conducted on an IEEE 34- bus three-phase ADN, verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.

Collaborative Optimization Allocation of Grid-Forming and Grid-Following Reactive Power Resources Considering Auxiliary Equipment Services

Collaborative Optimization Allocation of Grid-Forming and Grid-Following Reactive Power Resources Considering Auxiliary Equipment Services

Multi-Agent Deep Reinforcement Learning for Voltage Control With Coordinated Active and Reactive Power Optimization

The increasing penetration of distributed renewable energy resources causes voltage fluctuations in distribution networks. The controllable active and reactive power resources such as energy storage (ES) systems and electric vehicles (EVs) in active distribution networks play an important role in mitigating the voltage excursions. This paper proposes a two-timescale hybrid voltage control strategy based on a mixed-integer optimization method and multi-agent reinforcement learning (MARL) to reduce power loss and mitigate voltage violations. In the slow-timescale, the active and reactive power optimization problem involving capacitor banks (CBs), on-load tap changers (OLTC), and ES systems is formulated as a mixed-integer second-order cone programming problem. In the fast-timescale, the reactive power of smart inverters connected to solar photovoltaic systems and active power of EVs are adjusted to mitigate short-term voltage fluctuations with a MARL algorithm. Specifically, we propose an experience augmented multi-agent actor-critic (EA-MAAC) algorithm with an attention mechanism to learn high-quality control policies. The control policies are executed online in a decentralized manner. The proposed hybrid voltage control strategy is validated on an IEEE testing distribution feeder. The numerical results show that our proposed control strategy is not only sample-efficient and robust but also effective in mitigating voltage fluctuations.

Alternating Optimization Approach for Voltage-Secure Multi-Period Optimal Reactive Power Dispatch

This paper proposes an optimization approach for day-ahead reactive power planning to improve voltage security in transmission networks. The problem is formulated as a voltage-secure multi-period optimal reactive power dispatch (MP-ORPD) problem. The optimization approach searches for optimal set-points of dynamic and static reactive power (var) resources. Specifically, the output includes set-points for switching shunts, transformer taps, and voltage magnitudes at the regulated buses. The primary goal is to maximize the dynamic reactive power reserve of the system, by minimizing the reactive power supplied by synchronous generators. As the size of the MP-ORPD problem increases significantly with increasing number of contingencies and time periods, efficiency is crucial for practical applications. In this paper, a decomposition technique based on consensus and alternating optimization, where integer variable targets are obtained via MILP, is used to partition the MP-ORPD problem into a set of subproblems, which can be solved in parallel to reduce the computation time. The proposed MP-ORPD problem and its solution algorithm are integrated into the EPRI-VCA software. The results of various power networks of large electric utilities in the Eastern interconnection demonstrate the effectiveness of the proposed algorithm in providing preventive control schedules.

Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface

The Optimal Reactive Power Dispatch (ORPD) problem consists of finding the optimal settings of reactive power resources within a network, usually with the aim of minimizing active power losses. The ORPD is a nonlinear and nonconvex optimization problem that involves both discrete and continuous variables; the former include transformer tap positions and settings of reactor banks, while the latter include voltage magnitude settings in generation buses. In this paper, the ORPD problem is modeled as a mixed integer nonlinear programming problem and solved through two different metaheuristic techniques, namely the Mean Variance Mapping Optimization and the genetic algorithm. As a novelty, the solution of the ORPD problem is implemented through a Python-DIgSILENT interface that combines the strengths of both software. Several tests were performed on the IEEE 6-, 14-, and 39-bus test systems evidencing the applicability of the proposed approach. The results were contrasted with those previously reported in the specialized literature, matching, and in some cases improving, the reported solutions with lower computational times.

Leverage Reactive Power Ancillary Service Under High Penetration of Renewable Energies: An Incentive-Compatible Obligation-Based Market Mechanism

Power systems are undergoing fundamental changes with increasing penetration of renewable energies. A reactive power ancillary service market is of significance to incentivize private providers, such as thermal power units and renewable power plants (RPPs), to participate in maintaining voltage security. Because of the local nature of reactive power, one of the recognized issues is that monopolistic participants may have great market power and strategically manipulate their costs. To pursue economic efficiency, this paper proposes an incentive-compatible mechanism based on Vickery-Clarke-Groves (VCG) auctions for the day-ahead reactive power ancillary service market. The obligations of reactive power ancillary service are quantified in the VCG-based market mechanism. Depending on the expected performance of participants compared with the corresponding obligations, payments are obtained for thermal power units, RPPs, and loads. Hence, providers have the incentive to declare their true costs and undertake obligations. Furthermore, a scarcity price is employed to reward the provision of scarce reactive power resources and ensure reliable market clearing. The proposed mechanism is illustrated using the IEEE 24-bus and 118-bus systems. The results verify the effectiveness of the proposed mechanism. Moreover, high rewards or penalties are observed for voltage weak areas, thereby inducing appropriate investments in reactive power capacities.

Application of Sine–Cosine Optimization Algorithm for Minimization of Transmission Loss

On recent trends, the reactive power planning problem has received a considerable amount of attention for the allocation of reactive power resources, both static and dynamic such as switchable capacitors and/or reactors, as well as Var compensators, respectively. The reactive power has a significant impact on voltage profile, stability and transmission loss in power systems. In recent years, voltage instability is recognized as a major threat to system operation due to a shortage of reactive power in an interconnected transmission line. These voltage conditions could be analyzed and improved by proper coordination of reactive sources/sink. In this article, the minimization of transmission loss and operating cost is performed by using the sine cosine algorithm (SCA) and quasi-oppositional based sine cosine algorithm (QOSCA) technique for standard IEEE 14 and IEEE 30 and IEEE 57 bus test system of the connected power network. The proposed QOSCA reduces the transmission losses by 6.38% in IEEE 14, 4.56% in IEEE 30 and 12.77% in IEEE 57 bus system. The results obtained by QOSCA and SCA are compared with other popular techniques recently reported in recent state-of-literature. It is observed that QOSCA yields better results in terms of reducing transmission loss, operating costs, and improving the voltage profile of each bus.

Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem

The losses in electrical power systems are a great problem. Multiple methods have been utilized to decrease power losses in transmission lines. The proper adjusting of reactive power resources is one way to minimize the losses in any power system. Reactive Power Optimization (RPO) problem is a nonlinear and complex optimization problem and contains equality and inequality constraints. The RPO is highly essential in the operation and control of power systems. Therefore, the study concentrates on the Optimal Load Flow calculation in solving RPO problems. The Simple Particle Swarm Optimization (PSO) often falls into the local optima solution. To prevent this limitation and speed up the convergence for the Simple PSO algorithm, this study employed an improved hybrid algorithm based on Chaotic theory with PSO, called Chaotic PSO (CPSO) algorithm. Undeniably, this merging of chaotic theory in PSO algorithm can be an efficient method to slip very easily from local optima compared to Simple PSO algorithm due to remarkable behavior and high ability of the chaos. In this study, the CPSO algorithm was utilized as an optimization tool for solving the RPO problem; the main objective in this study is to decrease the power loss and enhance the voltage profile in the power system. The presented algorithm was tested on IEEE Node-14 system. The simulation implications for this system reveal that the CPSO algorithm provides the best results. It had a high ability to minimize transmission line losses and improve the system's voltage profile compared to the Simple PSO and other approaches in the literature.

Graph theory based transformation of existing Distribution network into clusters of multiple micro-grids for reliability enhancement

Enhancement of reliability is critical in designing and planning of distribution systems that operate efficiently and with minimal interruption to customer loads, because they use various types of resources and technologies to meet energy demand such as distributed generation (DG). DG is expected to play an enhanced role in emerging power systems. One of the major issues in the planning stage for future distribution systems is the optimal design of multiple micro-grids (MMGs) in distribution systems. This paper describes a novel method for clustering a traditional passive distribution system into a cluster of Multiple Micro Grids (MMGs). Clustering a traditional distribution system into MMGs provides numerous merits for costumers and distribution system operators, including a local control strategy to minimise interaction between various MGs, local reactive power compensation to prevent fault propagation, minimise power losses, and ultimately improving system reliability. In each MG area, DGs and reactive power resources are deployed to reach the acceptable level of reliability. The proposed clustering procedure is based on a weighted graph partitioning approach, with the weights representing the apparent power of the lines. Various reliability tests were performed in order to determine the best location for DG penetration in the distribution system. The passive distribution system becomes an active system after DG penetration. As a result, planners must investigate the influence of DG on distribution system reliability. The aim of this paper is to investigate the impact of DG on distribution system reliability. The results on the test distribution system demonstrate the proposed method’s performance and effectiveness.

Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization

This paper suggests leveraging the reactive power range embedded in wind farms to improve safety and optimality during the power system reactive power optimization process. First, three typical reactive power range approaches are analysed, and a two-stage robust linear optimization-based reactive power range evaluation method is proposed. This method yields a reactive power range that can be leveraged by an upstream system operator while ensuring wind farm operational security against wind farm uncertainty. Simplified DistFlow equations are employed to balance computational accuracy and cost. Next, an uncertain reactive power optimization problem that involves the wind farm reactive power range is introduced, through which system operators ensure system-wide security and optimality in the base case and against any possible deviation caused by system-wide load uncertainty and uncertain renewable generation. Steady-state models of automatic generation control and local voltage control against deviations are captured. This uncertain reactive power optimization problem is recast as a deterministic optimization problem that is readily solvable. Case studies confirm that even with notable uncertainty, wind farms are competent reactive power resources that provide a considerable reactive power range.

Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks

The principal motivation of the marine predators algorithm (MPA) is the common foraging technique, including Levy and Brownian motions in ocean predators coupled with optimal contact intensity policy in predator–prey biological interaction. This paper proposes an improved marine predators algorithm (IMPA), which is an extension of the original MPA. The suggested improvements lead to rapid convergence and avoid local minima stagnation for the original MPA. IMPA controls the active and reactive power injected into distribution systems to minimize the total system losses and the total voltage deviations and maximize the voltage stability and improve the distribution system's overall performance. On the one hand, the proposed IMPA determines the optimal location and active power (location and size, respectively) of distributed generation (DG). On the other hand, the IMPA controls reactive power by optimally placing and sizing the shunt capacitors (SCs) and determining the PF of DGs. Two standard test systems, 69-bus and 118-bus distribution networks, are considered to prove the proposed algorithm’s efficiency and scalability. Results of the proposed IMPA are compared with those obtained by MPA, AEO, and PSO algorithms. The findings of the simulation results demonstrate that the proposed IMPA can effectively find the optimal problem solutions and beats the other algorithms. Moreover, the framework of multi-objective IMPA outperforms based on MPA in terms of the performance measures of diversity, spacing, coverage, and hypervolume.

Short-Term Voltage Stability-Constrained Unit Commitment for Receiving-End Grid With Multi-Infeed HVDCs

With the increasing development of AC/DC hybrid power systems, local synchronous generators in receiving-end grids are being replaced by high-voltage direct current (HVDC) power. The risk of short-term voltage instability exists because of the decline in dynamic reactive power resources from generators, the presence of high-proportioned induction motors and the demand for vast reactive power of HVDC under AC faults. In this situation, unit commitment (UC) should take short-term voltage stability into account. Hence, crucial generators can be retained to provide sufficient dynamic reactive power. This paper proposes a UC model incorporating short-term voltage stability constraints (SVSCUC). Furthermore, a decomposition-based solution approach is presented, which involves an alternate iterative solving of UC and differential-algebraic equations (DAEs). Time-domain simulation (TDS) is utilized to quantify the stability with an indicator. Linear stable cuts are formulated through trajectory sensitivity analysis and added into the UC. The UC is decomposed into a master problem based on mixed-integer linear programming (MILP) and subproblems. The SVSCUC is illustrated using benchmark systems and a real system in eastern China. The results show the effectiveness and efficiency of the proposed methods. Violations of short-term voltage stability for credible contingencies are eliminated under the commitment of SVSCUC.

Optimal Fractional Order Based on Fuzzy Control Scheme for Wind Farm Voltage Control with Reactive Power Compensation

Voltage stability margin is ensured through the reactive power resources. In order to generate the reactive power references and ensure the low-voltage ride-through (LVRT) control of a wind farm system based on squirrel cage induction generator, this paper proposed an optimal control approach based on fractional-order (FO) PI-fuzzy-PI (FOPI-fuzzy-FOPI) controller. The proposed control method ensures, also, the demand for active and reactive power predetermined by the transmission system operator (TSO) and satisfies the grid code recommendations. In order to achieve a faster tracking of state variables of the system, the FO operators are optimized using the particle swarm optimization algorithm (PSOA). Using FO operator and PSOA, the responses of the system can be improved. The proposed controller provides additional parameters for better tracking performance and faster convergence can be achieved. Numerical simulation results are presented to analyze the advantages of the proposed control approach to design a physically, realizable controller. The present results are compared with various control methods to show the superiority of the method proposed in this paper.

Reactive coordinated optimal operation of distributed wind generation

Large-scale distributed wind generation (DWG) integration brings new challenges to the optimal operation of the distribution network. The reactive supports from wind turbines (WTs) and reactive power resources can improve both the operation economy and renewable energy consumption. In this paper, a multi-period reactive coordinated optimal operation model for DWG in the distribution network is established. The active-reactive power coordination characteristics of two typical types of WTs are considered and the operating strategy of reactive power resources is integrated in the model. The second-order cone programming (SOCP) is developed to transform the original nonlinear power flow model into a linear and convex model, which would significantly improve the power flow calculation efficiency for DWG penetrated distribution network. The simulation results show that the integration of reactive power resources can further promote the consumption of DWG and improve the operating profits of the distribution network.

Sensitive reactive power dispatch solution accomplished with renewable energy allocation using an enhanced coyote optimization algorithm

This study is concerned with solving the optimal reactive power dispatch (ORPD) problem considering the existence of renewable energy resources (RERs). The sensitivity analysis aims at studying the impact of incorporating reactive power resources and wind units for solving the ORPD problem. An enhanced coyote optimization algorithm (ECOA) is developed for solving ORPD problem. The ECOA combines the merits of fuzzy logic principles and the conventional COA with the aim of obtaining the best performance of ORPD solution. The capability of ECOA are examined on IEEE 30-bus, 118-bus test systems while its scalability is tested for standard IEEE 300-bus test system. The impact of different penetration levels of wind energies on achieving the ORPD is assessed. Statistical analyses are carried out to prove the robustness of the proposed ECOA. Therefore, the ECOA leads to more competitive solutions for single and multi-objective cases compared with the reported methods in the literature.