Reactive Power Scheduling Research Articles

An AC optimal power flow framework for active–reactive power scheduling considering generator capability curve

AbstractThis paper presents a simplified optimal power flow (OPF) framework to facilitate co‐optimised active and reactive power scheduling for synchronous generators and price‐sensitive demands. The proposed framework creates an opportunity for generators and loads to simultaneously participate in a combined market for active and reactive power. The co‐optimisation of active and reactive power generation is constrained by the interdependence of the active and reactive power capacities, which is represented by the generator capability curve. Thus, a detailed mathematical derivation of the opportunity costs across various regions of the generator capability curve is presented. This study considers a detailed generator capability curve that considers the armature, field, under‐excitation, and prime mover limits. The interdependence of active and reactive power consumption for demand is modelled using the concept of power‐factor. The OPF problem for generator and load scheduling is formulated as a non‐linear optimisation task, leveraging the inherent properties of the generator capability curve, that is, piecewise smoothness, continuity, and the monotonically increasing slope magnitudes. Furthermore, to simplify the OPF formulation, the non‐linear capability curve is represented as a combination of the linear curves. To demonstrate the effectiveness of the proposed OPF methodologies, suitable case studies are conducted using different test systems.

Formation of Exact Co-ordination Equations with Proper Selection of Design Variables for Active and Reactive Power Scheduling

The Design variables in objective function must be an independent variables for best solutions in Pwer system Optimization Problems, whatever may be the mathamatical and computation Techniques. Selection of entire ingected Generator (Source) powers in to the network are not independent variables since the naetwork loss is present in the generator powers which depends on how the generators share the total system load. The work presents the revision of fundamental coordination equations to prove the concept.The genarator contributions to the system load are considered as design(independent variables).An example is given to ilstrate the application of the prposed derived equqtions.

Two-stage scheduling of smart electric vehicle charging stations and inverter-based Volt-VAR control using a prediction error-integrated deep reinforcement learning method

Smart electric vehicle charging stations (EVCSs) having distributed energy resources (DERs), including photovoltaic (PV) systems and energy storage systems (ESSs), are becoming vital devices for increasing their profit and maintaining stable distribution grid operations by scheduling the real/reactive power of DERs. However, prediction errors of PV generation outputs and electric vehicle (EV) loads from EVCSs may decrease their profit and destabilize the distribution grid owing to incorrect EV charging scheduling and voltage regulation. To address this issue, we propose a two-stage framework for smart EVCS scheduling and inverter-based Volt-VAR control (VVC) using prediction error-integrated deep reinforcement learning (DRL). In the first stage, the EVCS agents train their neural network model with a 30-min resolution to maximize their profit through a day-ahead charging/discharging scheduling of ESSs in the EVCSs while responding to various prediction errors of PV generation outputs and EV loads. The total real power consumption of each EVCS, including the charging/discharging schedules of the ESSs calculated in the first stage, is delivered to the second stage, in which the VVC agent trains its neural network model with a 5-min resolution to minimize the real power loss and voltage violations through real-time reactive power scheduling of the ESSs in the EVCSs via their inverters. The proposed approach was tested in the IEEE 33-node and IEEE 123-node distribution systems. The results show that the proposed approach outperforms DRL methods that do not consider the prediction errors in terms of profitability of the EVCS and reduction of real power loss.

Strategic active and reactive power scheduling of integrated community energy systems in day-ahead distribution electricity market

The increasing penetration of distributed energy resources (DERs) enable integrated community energy systems (ICESs) as emerging techno-economic energy entities at the end-user side. In the deregulated electricity market, the ICESs can actively participate in the distribution electricity market (DEM) to provide active and reactive power dispatched by ICES operator (ICESO). In this context, a strategic active and reactive power scheduling model for ICESs in the day-ahead DEM is proposed in this paper. A novel trading mechanism designed for ICESO and distribution system operator (DSO)’s interaction toward both active and reactive power is represented in the day-ahead DEM, where distribution locational marginal prices (DLMPs) for both active and reactive power are introduced as the market settlements. Then, the trading process is formulated as a bi-level programming problem. The upper level describes the combined cooling, heating, and power (CCHP)-dominated ICESO’s strategic active and reactive power dispatch, in which the DLMPs for active and reactive power, internal flexibility services, and optimal dispatch for inverter-based distributed generators are considered. The lower level performs a DEM clearing for both active and reactive power. Further, Karush–Kuhn–Tucker (KKT) conditions, Big-M approach, and duality theory are used to convert the bi-level model from a Mathematical Programing with Equilibrium Constraints (MPEC) model to a single-level mixed-integer second-order cone programming (MISOCP) model for efficient calculation. Finally, case studies on modified IEEE 33-node and IEEE 123-node distribution systems are adopted to evaluate the performance of the proposed scheduling method. The results show that the proposed scheduling approach can contribute to the renewable energy share and energy efficiency, reduce CCHP-dominated ICESs’ operation costs by 38.13% and 13.21% compared to the case where ICESO only acts as a price-taking consumer, and 6.64% and 3.44% compared to the case where ICESO does not consider reactive power bids/offers, respectively.

IGDT-Based Robust Operation of Thermal and Electricity Energy-Based Microgrid with Distributed Sources, Storages, and Responsive Loads

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties.

Stochastic scheduling of energy storage systems in harmonic polluted active distribution networks

Harmonics originating from non-linear loads in distribution systems will cause undesired effects and malfunctions of equipment. It is important to use efficient techniques for eliminating the voltage and current harmonic components. However, the uncertain nature of the active distribution networks (ADN) makes it difficult to apply these techniques, efficiently. Renewable energy sources (RESs), such as wind turbines (WTs) besides load fluctuations are the most important uncertainty sources. This study employs energy storage systems (ESS) to reduce total harmonic distortion (THD) as well as the ADN operating costs. In addition to the ESS, network structure reconfiguration and reactive power scheduling is presented for a more efficient operation of ADN. Also, the uncertainty of the network is modelled by the K-means and LHS methods. This methodology is conducted under a multi-objective optimization problem, which is handled by a multi-objective particle swarm optimization (MOPSO) algorithm. Furthermore, a technique for order of preference by similarity to ideal solution (TOPSIS) approach is applied to obtain the best compromise between two objectives. The efficacy of the proposed methodology is investigated in the IEEE 33 bus test system. Also, for validating the results, a large-scale network, the IEEE 118 bus test system is surveyed, too.

Security Constrained Reactive Power Scheduling Considering N-1 Contingency of Transmission Lines

This paper presents a methodology for reactive power scheduling (RPS) of power system in the form of AC optimal power flow (AC-OPF) problem. The objective function is minimization of system total active power losses. The OPF optimally determines reactive power output of generating units and synchronous condensers, tap-changers ratio, shunt capacitor banks and reactors. The effect of tap-changer is modeled in the active and reactive power flow of transformer. The proposed method grantees secure operation of system in normal operating condition and also in contingency of transmission line outage. The validity of proposed method is studied based on IEEE RTS 24-bus. Results show the capability of suggested AC-OPF for RPS of system in base case as well as contingency of single line outage.

A Multiport Electric Energy Routing Scheme Applied to Battery Energy Storage System

In this paper, the research status of topology and control strategy of energy storage grid-connected system is analyzed, and aiming at the working characteristics of the repurposed battery, a cascade power electronic transformer (CPET) with independent DC output is proposed. The working principle of current fed isolated bidirectional DC-DC converter (CF-IBDC) and cascaded H-bridge (CHB) is analyzed, and the decoupling control strategy is designed. In this paper, a hierarchical control strategy is designed for the repurposed battery energy storage (RBES) grid-connected system based on CPET, which consists of three layers: energy layer, power layer, and state of charge (SOC) layer. The energy layer responds to active and reactive power scheduling instructions, the power layer controls the grid-connected current and tracks the grid voltage, and the SOC layer equates the charged state of repurposed batteries. A 3 MVA/12 kV three-phase grid-connected simulation system was established, and a 1 kW single-phase system experiment platform was designed. The simulation and experimental results can verify the correctness of the theoretical analysis and the feasibility of the control strategy.

Reactive Power Management for Networked Microgrid Resilience in Extreme Conditions

The inadequate level of reactive power generation by distributed energy resources (DERs) in extreme conditions could render networked microgrids (NMGs) more susceptible to major contingencies. In extreme conditions when the NMGs islanding from the main grid might occur, the inadequacy of reactive power generation could make it more difficult for NMGs operators to maintain a reasonable margin for the resilient NMGs operation. This paper studies the resilient reactive power scheduling of NMGs in extreme conditions. A two-stage model is proposed which applies Here-And-Now (HAN) and Wait-And-See (WAS) decisions at the first and the second stages, respectively. The HAN and WAS stages are linked through a stochastic programming approach where the entire problem is formulated in mixed-integer linear programming. To address scalability and privacy concerns in NMGs operations, the proposed two-stage model is decentralized and decomposed into a master problem and three subproblems and linked by Benders cuts. The master problem identifies the least-cost operation plan for NMGs considering normal and extreme conditions at Stages 1 and 2. Subproblems I, II, and III satisfy the feasibility, security, and optimality of individual microgrid operations, respectively. The DER limitations in supplying reactive loads are more evident at Stage 2 during the islanding simulation, which is managed by the proposed extreme condition reactive power procurement approach. The effectiveness of proposed two-stage model in enhancing the resilience of the NMGs is assured by comprehensive simulation studies.

Phase unbalance and PAR constrained optimal active and reactive power scheduling of Virtual Power Plants (VPPs)

In near future, the integration of unknown and unpredictable quantity of Electric Vehicles (EVs) can violate reliable and quality power service of distribution system. In spite of that, ability of simultaneously regulate active and reactive power by Plug-in-Hybrid Electric Vehicles (PHEVs) in quick response time, without affecting the batteries can help to ensure reliable and quality power service in distribution system. This paper investigates the optimal active and reactive power scheduling of PHEVs and Distributed Generations (DGs) in Virtual Power Plants (VPPs), considering unbalance and Peak-to-Average Ratio (PAR) constraints. To compute stochastic model of PHEVs, we considers dynamic nature of driving pattern based on NHTS 2017 data. The proposed approach is implemented on IEEE-25 bus unbalanced distribution system. The developed planning and operational investigation of VPPs also presents dependencies of cost and losses in terms of Unbalance factor and PAR.

A Novel VSG-Based Accurate Voltage Control and Reactive Power Sharing Method for Islanded Microgrids

Islanded microgrids (IMGs) are more likely to be perturbed by renewable generation and load demand fluctuation, thus leading to system instability. The virtual synchronous generator (VSG) control has become a promising method in the microgrids stability control area for its inertia-support capability. However, the improper power sharing and inaccurate voltage control problems of the distributed generations (DGs) in microgrids still has not been solved with a unified method. This paper proposes a novel VSG equivalent control method named Imitation Excitation Control (IEC). In this method, a multi-objective control strategy for voltage and reactive power in a low voltage grid that considers a non-negligible resistance to reactance ratio (R/X) is proposed. With the IEC method, the voltage drop across feeders is compensated, thus the terminal voltage of each inverter will be regulated, which will effectively stabilize the PCC (point of common coupling) voltage and inhibit the circular current. Meanwhile, this method can realize accurate reactive power tracking the reference value, making it accessible for reactive power scheduling. What is more, the reasonability of the IEC model, namely the equivalent mechanical characteristic and transient process inertia support between VSGs and conventional synchronous generators (SG), is illustrated in this paper. Moreover, steady-state stability is proved by the small-signal modeling method, and the energy required by inertia support is given. Finally, the simulation result validates the effectiveness of the proposed method, and it is also demonstrated that the proposed method outperforms the conventional droop control method.

Probabilistic day-ahead simultaneous active/reactive power management in active distribution systems

Distributed generations (DGs) are main components for active distribution networks (ADNs). Owing to the large number of DGs integrated into distribution levels, it will be essential to schedule active and reactive power resources in ADNs. Generally, energy and reactive power scheduling problems are separately managed in ADNs. However, the separate scheduling cannot attain a global optimum scheme in the operation of ADNs. In this paper, a probabilistic simultaneous active/reactive scheduling framework is presented for ADNs. In order to handle the uncertainties of power generations of renewable-based DGs and upstream grid prices in an efficient framework, a stochastic programming technique is proposed. The stochastic programming can help distribution system operators (DSOs) to make operation decisions in front of existing uncertainties. The proposed coordinated model considers the minimization of the energy and reactive power costs of all distributed resources along with the upstream grid. Meanwhile, a new payment index as loss profit value for DG units is introduced and embedded in the model. Numerical results based on the 22-bus and IEEE 33-bus ADNs validate the effectiveness of the proposed method. The obtained results verify that through the proposed stochastic-based power management system, the DSO can effectively schedule all DGs along with its economic targets while considering severe uncertainties.

A generalized short-term unit commitment approach for analyzing electric power and natural gas integrated systems

This paper proposes a generalized short-term unit commitment approach for optimizing the hourly thermal generation scheduling by considering the existing interdependence between natural gas and electricity infrastructures. This generalized approach is formulated as a mixed-integer nonlinear optimization problem where a multi-period optimal gas and alternating current power flow model is incorporated into the thermal unit commitment problem. Contrary to all other proposals that ignore the reactive power dispatch and alternating current electricity network constraints for solving the unit commitment problem that considers the integrated electricity-natural gas system, the proposed solution approach simultaneously co-optimizes the active and reactive power scheduling and dispatch, while taking into account the set of physical and operational constraints associated with each infrastructure. This set of constraints includes reactive power generation limits, nodal voltage magnitude limits and dynamic gas flow constraints. The solution is obtained by decomposing this generalized optimization problem into two mutually connected optimization subproblems: one spatially separable related to the unit commitment problem and the other temporally separable associated with the unified co-optimization of both energy systems, which are sequentially solved until the same hourly generation dispatch in both subproblems is obtained. Hence, this solution is optimal for the unit commitment problem and the optimal gas and power flow problem. Study cases on two multi-energy systems are presented to numerically illustrate the suitability and main characteristics of the proposed approach.

Loss Minimization in Optimal Reactive Power Planning (ORPP) Based on Whale Optimization Algorithm

The key of RPP is the optimal allocation of reactive power considering location and size. This paper presents the loss minimization in optimal reactive power planning (ORPP) based on Whale Optimization Algorithm (WOA). The objective is to minimize transmission loss by considering several load conditions at bus 3, bus 15 and bus 21. Reactive Power Scheduling (RPS) and Transformer Tap Changer Setting (TTCS) were set as the control variables. Validation was conducted on the IEEE 30 Bus RTS. Results from the study indicate that the proposed WOA can minimize transmission loss better than Evolutionary Programming (EP).

All-day coordinated optimal scheduling in distribution grids with PV penetration

This paper presents an approach for the optimal scheduling of reactive power of photovoltaic (PV) inverters, tap position of on-load tap changers (OLTCs), and switch states of shunt capacitors (SCs) in distribution grids. The proposed method determines a day-ahead scheduling strategy containing continuous, discrete, and Boolean control variables. The optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem with the objective of minimizing both the node voltage deviations and active power losses. The maximum allowable number of operations for OLTCs and SCs is constrained in predefined limits. Due to the operation limits of OLTCs and SCs, this multi-period optimal scheduling problem is time-coupled. The pattern search algorithm and the genetic algorithm are applied to solve the proposed optimization problem. Both the algorithms are improved with the multi-start framework for global optimization. The feasibility of the proposed method is examined on the modified IEEE 34-node test feeder and the modified IEEE 123-node test feeder. The performances of the proposed approach are verified in the case of forecast errors of PV generation and load demand.

Active and Reactive Power Scheduling Optimization using Firefly Algorithm to Improve Voltage Stability under Load Demand Variation

This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.<span style="font-size: 9pt; font-family: Arial, sans-serif;" lang="EN-US">This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.</span>

Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

A Comprehensive Review on Economic Load Dispatch using Evolutionary Approach

Economic Load Dispatch, ELD can be defined as the way of allocating the load level to the generators of the power plant in such a way that the total demand would be supplied in a most economic manner and completely. In a practical power system, the power plants are not located at the same distance from the centre of loads and their fuel costs are different. Also, under normal operating conditions, the generation capacity is more than the total load demand and losses. Thus, there are many options for scheduling generation. In an interconnected power system, the objective is to find the real and reactive power scheduling of each power plant in such a way as to minimize the operating cost. This means that the generator‟s real and reactive powers are allowed to vary within certain limits so as to meet a particular load demand with minimum fuel cost. This is called optimal power flow problem. In this paper, Economic Load Dispatch (ELD) of real power generation is considered. Economic Load Dispatch (ELD) is the scheduling of generators to minimize total operating cost of generator units subjected to equality constraint of power balance within the minimum and maximum operating limits of the generating units. This paper gives a survey of research work covering the concept of economic load dispatch. Economic load dispatch gives the best saving in cost for any power generation plant operation in which the methodology can be applied by various means from conventional to the advanced. In the past years up to 90s, the conventional techniques were used to make this happen but in the past decades AI techniques have fulfilled the requirements with satisfactory results that are being reviewed.

The Unit Commitment Problem With AC Optimal Power Flow Constraints

We propose a mathematical programming-based approach to optimize the unit commitment problem with alternating current optimal power flow (ACOPF) network constraints. This problem is a nonconvex mixed-integer nonlinear program (MINLP) that we solve through a solution technique based on the outer approximation method. Our solution technique cooptimizes real and reactive power scheduling and dispatch subject to both unit commitment constraints and ACOPF constraints. The proposed approach is a local solution method that leverages powerful linear and mixed-integer commercial solvers. We demonstrate the relative economic and operational impact of more accurate ACOPF constraint modeling on the unit commitment problem, when compared with copperplate and DCOPF constraint modeling approaches; we use a six-bus, the IEEE RTS-79, and the IEEE-118 test systems for this analysis. Our approach can be extended to solve larger scale power systems as well as include security constraints or uncertainty through decomposition techniques.

Fuzzy-based Strategy for Voltage and Reactive Power Scheduling

Background / Objectives: This paper narrates a novel strategy employing fuzzy logic for voltage profile enhancement and loss minimization in power systems. Linearized model is utilized and limit violations of control variables translated into fuzzy set notation. Methods/Statistical Analysis: The method adopted here for the voltage profile enhancement consists of certain number of load buses with definite VAR compensation schemes. In this work, it is mainly aimed to enhance the system voltage profile and thereby reducing power losses in it. Here, the linearization of objective functions and constraints is done about the current operating point. Findings: The equality and inequality constraints include dependent and independent variables. Later, power flow studies are used to calculate the limit violations of bus voltage magnitude and thereby a viable solution set that aims for voltage profile improvement as well as power loss reduction is finally obtained using appropriate operations of the fuzzy sets. In order to demonstrate the efficacy of this approach, IEEE 14 –bus test system is implemented. Application/Improvement: Due to the application of fuzzy logic, the control of variables in the problem is global and it involves less computational time and the results can have more accuracy.