Volt-var Control Research Articles

Fair coordination of distributed energy resources with Volt-Var control and PV curtailment

This paper presents a novel distributed optimal power flow (DOPF) method for fair distributed energy resource (DER) coordination in the context of mandated rooftop PV inverter control modes. In practice, inverter reactive power control is increasingly required by grid connection codes, which often unfairly curtail PV generation of prosumers towards the end of low-voltage feeders. Similarly, optimization-based DER coordination methods that aim solely for technically-efficient DER coordination do not consider the distribution of PV curtailment across customers. To address these concerns, we develop a tractable multi-objective DOPF method for optimal DER coordination that (i) curtails PV generation fairly across prosumers, and (ii) incorporates a standard piecewise-linear Volt-Var control reactive power control function without using integer variables. Three equity principles representing different interpretations of fairness are implemented in our coordination method; namely, egalitarian, proportional and uniform dynamic PV curtailment redistribution. The performance of our approach is demonstrated on low-voltage distribution feeders of different sizes (5, 10, 25, 50 and 100 prosumers) using two network topologies: line topology without lateral spurs and tree topology with lateral spurs. Each network considers three levels of PV penetration, giving 30 test systems in total. The results demonstrate the effectiveness of the proposed DOPF method for fair DER coordination: PV curtailment is equitably distributed among prosumers with a computational burden on par with conventional DOPF approaches. Moreover, different fairness methods result in different patterns of curtailment, which a regulator may choose between.

수용용량과 계통 손실을 고려한 스마트 인버터의 최적 Volt-VAR 제어 곡선 선정에 관한 연구

The penetration of Renewable Energy Source (RES) has been increasing because RES is drawing attention by the environmental problem in the worldwide. The power flow can be changed from unidirectional to bidirectional because of increasing the penetration of RES. In bidirectional system, the efficient operation of conventional Voltage Regulators (VRs) is difficult. As one of the various methods to reinforce the conventional VR, the Volt-VAR control of the Smart Inverter (SI) is used for supporting the voltage by using the reactive power of RES. In this paper, the Volt-VAR control curve with the modified dead band is analyzed for the factors affecting the Hosting Capacity (HC) and the enhanced method to obtain the maximum HC. The modified Volt-VAR control curve is applied and the power flow is calculated through Open source Distribution System Simulator (OpenDSS). The maximum HC is obtained through Genetic Algorithm (GA) using MATLAB.

Two-Stage Volt/Var Control in Active Distribution Networks With Multi-Agent Deep Reinforcement Learning Method

The high penetration of intermittent renewable energy resources in active distribution networks (ADN) results in a great challenge for the conventional Volt-Var control (VVC). This article proposes a two-stage deep reinforcement learning (DRL)-based real-time VVC method to mitigate fast voltage violation while minimizing the network power loss. In the first stage, on-load tap changer (OLTC) and capacitor banks (CBs) are dispatched hourly based on the optimal power flow method. The optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) which can be effectively solved. In the second stage, the reactive power of photovoltaics (PVs) is regulated dynamically to mitigate fast voltage fluctuation based on the well-learned control strategy and local measurements. The real-time VVC problem is formulated and solved using a multi-agent deep deterministic policy gradient (MADDPG) method, which features offline centralized training and online decentralized application. Rather than using the critic network to evaluate the output of the actor-network, the gradient of the action-value function to action is derived analytically based on the voltage sensitivity method. The proposed approach is tested on the IEEE 33-bus distribution system and comparative simulation results show the enhanced control effect in mitigating voltage violations.

Reactive Power Planning in Distribution System Using Genetic Algorithm.(Dept.E)

This paper presents a solution for the Volt/VAR control (VVC) problem in distribution system using Genetic Algorithm (GA). The problem is divided into two parts. In the first part the candidate buses for reactive power insertion are determined. In the second part the locations, number and sizes of reactive power sources are determined in an optimal manner. The solution method has been implemented and tested with a 12-bus IEEE test system.

Data-driven inverter-based Volt/VAr control for partially observable distribution networks

For active distribution networks (ADNs) integrated with massive inverter-based energy resources, it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ADNs. Thus, current models of ADNs are usually involving significant errors or even unknown. Moreover, ADNs are usually partially observable since only a few measurements are available at pilot nodes or nodes with significant users. To provide a practical Volt/Var control (VVC) strategy for such networks, a data-driven VVC method is proposed in this paper. Firstly, the system response policy, approximating the relationship between the control variables and states of monitoring nodes, is estimated by a recursive regression closed-form solution. Then, based on real-time measurements and the newly updated system response policy, a VVC strategy with convergence guarantee is realized. Since the recursive regression solution is embedded in the control stage, a data-driven closed-loop VVC framework is established. The effectiveness of the proposed method is validated in an unbalanced distribution system considering nonlinear loads where not only the rapid and self-adaptive voltage regulation is realized but also system-wide optimization is achieved.

A Day-Ahead Chance Constrained Volt/Var Control Scheme With Renewable Energy Sources by Novel Scenario Generation Method in Active Distribution Networks

With the integration of huge renewable energy sources (RESs) into active distribution networks, how to address the uncertainty outputs of RESs for the day-ahead volt/var control (VVC) is a significant challenge. This paper presents a chance constrained mixed integer second order cone model to handle the nodal power uncertainties and nonlinear branch flow equations. A direct and fast scenario generation method is proposed by employing the group division method and the seven-step probability distribution model of RESs outputs. An efficient and accurate solution method which only uses few larger probability level scenarios instead of all reserved scenarios is also proposed. Numerical simulations on the IEEE 69 standard system show the superiority of the proposed algorithm over traditional Monte Carlo sampling (MCS)-based methods.

Techno-Economic Assessment of Grid-Level Battery Energy Storage Supporting Distributed Photovoltaic Power

Centralised, front-of-the-meter battery energy storage systems are an option to support and add flexibility to distribution networks with increasing distributed photovoltaic systems, which generate renewable energy locally and help decarbonise the power sector. However, the provision of specific services at distribution level remains under development for real applications in industry. To this end, this paper presents an exhaustive techno-economic analysis of the role of front-of-the-meter battery energy storage systems in primary distribution networks with presence of distributed PV covering: (i) the siting decision for storage systems using multi-objective genetic algorithm optimisation; (ii) the response when smart capabilities for PV inverters (e.g., volt-var control) are present; and (iii) the quantification of revenue streams and compensation schemes that would bring positive profitability when providing distribution-specific services based on the supply of real and reactive power. The performance of grid-level battery energy storage technology is evaluated in the IEEE 34-bus system particularised to the distribution code of Northern Ireland, UK. The techno-economic assessment covers one year of simulations at 1-minute resolution performed with the simulation tool EPRI OpenDSS and its Python communication interface. The results show that, besides the technical benefits of centralised battery storage systems, the economic compensation based on traditional transmission-level services is not sufficient for the profitability of these projects. Several ideas are explored to improve the profitability of these systems considering the local value and decarbonisation they add. The findings presented can direct system operators and regulators towards developing schemes to incentivise centralised battery energy storage projects in distribution networks in the context of distribution-level services.

Two-Stage Deep Reinforcement Learning for Inverter-Based Volt-VAR Control in Active Distribution Networks

Model-based Vol/VAR optimization method is widely used to eliminate voltage violations and reduce network losses. However, the parameters of active distribution networks(ADNs) are not onsite identified, so significant errors may be involved in the model and make the model-based method infeasible. To cope with this critical issue, we propose a novel two-stage deep reinforcement learning (DRL) method to improve the voltage profile by regulating inverter-based energy resources, which consists of offline stage and online stage. In the offline stage, a highly efficient adversarial reinforcement learning algorithm is developed to train an offline agent robust to the model mismatch. In the sequential online stage, we transfer the offline agent safely as the online agent to perform continuous learning and controlling online with significantly improved safety and efficiency. Numerical simulations on IEEE test networks not only demonstrate that the proposed adversarial reinforcement learning algorithm outperforms the state-of-art algorithm, but also show that our proposed two-stage method achieves much better performance than the existing DRL based methods in the online application.

Development of an Encoding Method on a Co-Simulation Platform for Mitigating the Impact of Unreliable Communication

This article presents a hardware-in-the-loop (HIL) based modeling approach for simulating impacts of unreliable communication on the performance of centralized volt-var control and for developing an encoding method to mitigate the impacts. First, an asynchronous real-time HIL simulation platform is introduced to enable multi-rate co-simulation of a distribution system with many inverter-based distributed energy resources (DERs). The distribution system is modeled by milliseconds phasor-based models and the DERs are modeled by micro-seconds power electronic models. Communication connections between a centralized volt-var controller (modeled externally to the HIL testbed) and smart inverters are built by implementing Modbus links and the Long Term Evolution network. On this co-simulation platform, an enhanced, augmented Lagrangian multiplier based encoded data recovery (EALM-EDR) algorithm for mitigating the impact of unreliable communication is developed and validated. Simulation results demonstrate the efficacy of using the HIL-based co-simulation platform as a power grid digital twin for developing algorithms that coordinate a large number of heterogeneous control systems through wired and wireless communication links.

A Spatiotemporal Data Summarization Approach for Real-Time Operation of Smart Grid

In a smart grid distribution management system, operation, planning, forecasting and decision making relies on demand-side management functions, which require real-time smart grid data. This data has significant dollar value because it is extremely useful for efficient control and intelligent prediction of the energy consumption, and expert management of residential and commercial load. However, the huge amount of (smart grid) data generated at a very high velocity poses a number of challenges. Utility companies have a huge demand for efficient summarization techniques to mine interesting patterns and extracting useful and actionable intelligence. Research from various domains has shown that data summarization can significantly improve the scalability and efficiency of various data analytic tasks (e.g., transactional database mining, data streams mining, network monitoring). This paper proposes a summarization approach (i.e., a set of algorithms, data structures, and query mechanisms) that enables the utility company to accurately infer various energy consumption patterns in real-time by automatic monitoring of smart grid data using significantly less computational resources. The proposed summarization approach is suitable for processing spatiotemporal streams, and it can also provide answers in real-time to various smart grid applications (e.g., demand-side management, direct load control, smart pricing and Volt-VAr control). Both theoretical bound and experimental evaluation are presented in this paper, which shows that the memory required for the proposed data structure grows linearly for the first 52 weeks; but interestingly, after the first year, the memory growth is negligible. The experimental results show that the proposed approach can process around 4 million smart meter readings every second or 120 million readings every minute. The proposed approach outperforms widely commercially used Database Management Systems (DBMSs) in terms of update and query costs: it is about 200 times faster than DBMSs in terms of update time, and about 340 times faster than DBMSs in terms of query time.

Evaluation of sensitivity based coordinated volt-var control and local reactive power for voltage regulation and power exchange across system boundaries in smart distribution networks

This paper presents a hierarchical coordinated volt-var controller (VVC) at medium voltage level for the containment of voltages at the critical nodes in a smart distribution network. VVC finds the optimal set-points for distributed generators and tap position of the transformers in order to contain the voltage at the critical nodes with-in limits and to minimize the total reactive power dispatched. In addition to the voltage regulation, VVC can track reactive power as a reference signal to control the power exchange across system boundaries. The proposed approach is compared with the optimal power flow that requires load flow calculation for each control update. In comparison, VVC is based on a sensitivity matrix based approach that reduces the complexity of the problem. The paper presents separate and combined evaluation of the proposed VVC with reactive power support from inverters at low voltage in order to quantify the combined impacts. The comparison is performed based on the voltage deviation and performance indices, line loading and reactive power exchange at the system boundary. The simulations spanning one year are performed on a synthetic network. The study leads to comprehensive insights into factors influencing the effectiveness of the reactive power control strategies.

An Operational Approach to Multi-Objective Optimization for Volt-VAr Control

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitor banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, a general formulation of the multi-objective Volt-VAr Optimization problem is proposed. The applicability of various multi-optimization techniques is considered and the operational interpretation of these solutions is discussed. The methods are demonstrated using a simulation on a test feeder.

A Multi-Stage Coordinated Volt-Var Optimization for Integrated and Unbalanced Radial Distribution Networks

The growing penetrations of rooftop photovoltaics (PVs) into low-voltage (LV) distribution networks are challenging voltage regulation. Developing an effective volt-var (VV) control has been the focus of many researchers with various approaches proposed so far. However, assuming a single voltage level and balanced network model, widely adopted in existing literatures, tends to cause inaccurate and even infeasible control solutions. Besides, existing distribution VV control studies are usually based on the day-ahead predictions of PV generations and loads, introducing inevitable and non-negligible errors. To address the challenges above, this paper proposes a VV co-optimization across unbalanced medium-voltage (MV) and LV networks, by traditional and emerging techniques, to ensure the network operation with the required power quality. Specifically, the operation of MV delta-connected switched capacitors and LV distributed PV inverters is coordinated, under a three-stage strategy that suits integrated and unbalanced radial distribution networks. The proposal aims to simultaneously improve voltage magnitude and balance profiles while reducing network power loss, at the least controlling cost. To effectively solve the proposed VV optimization problem, a joint solver of the modified particle swarm optimization and the improved direct load flow is employed. Finally, the proposal is evaluated by simulations on real Australian distribution networks over 24 h.

A decentralized optimization method based two-layer Volt-Var control strategy for the integrated system of centralized PV plant and external power grid

The large-scale integration of centralized photovoltaic (PV) plant (CPVP) is encountering severe challenges. On the one hand, the weak static voltage stability (SVS) of the external power grid (EPG) greatly restricts the maximum transmission power provided by CPVP. On the other hand, voltage limit violation is prone to occur at the end of collecting power lines inside CPVP. Driven by the above voltage issues, this paper proposes a novel two-layer Volt-Var control (VVC) strategy with two timescales for the integrated system consisting of CPVP and EPG. In this VVC strategy, besides the interaction between CPVP and EPG is fully considered, the coupling between transmission network (TN) and distribution network (DN) in EPG is also highly valued. The explicit integrated optimization model is constructed. Two decentralized optimization methods are applied for efficiently solving the integrated optimization problems corresponding to the different control layers, respectively. The operations of various VVC devices are coordinated via the proposed VVC strategy. The simulation results obtained indicate that the proposed two-layer VVC strategy can effectively reduce the voltage limit violation risk inside CPVP, and even in the worst case, the average nodes voltage deviation is only 0.024 p.u. Meanwhile, compared with the case without VVC, the value of the selected average SVS margin index will be reduced by about 33.18%, which means EPG is significantly improved.

Operational reliability assessment of photovoltaic inverters considering voltage/VAR control function

This paper proposes an operational reliability assessment approach of photovoltaic (PV) inverters considering a voltage/VAR control (VVC) function. The approach aims to quantify the reliability degradation and estimate the lifetime of PV inverters when they are utilized for the VVC function. Firstly, an inverter based VVC model considering uncertain profiles of PV power generation and loads is developed and it is solved by a stochastic optimization method. Then, a long-term reliability assessment procedure of the inverters utilized in the VVC model is proposed. To assess the reliability, a power loss model considering the VVC results is developed and a modified lifetime model based on Coffin-Manson model and manufacturing information is proposed. The proposed VVC model and inverter reliability assessment approach are verified on a 33-bus distribution network. The simulation results reveal the long-term impacts of additional utilization in the VVC function on operational reliability and lifetime of PV inverters.

Demand Side Management Strategy for Distribution Networks Volt/Var Control: A FCS-Model Predictive Control Approach

Due to the advent of distributed energy resources at distribution level, utility companies face new challenges in terms of voltage control. Existing works in the literature have proposed the use of demand side management (DSM) to provide ancillary services to the grid. Some works explored the use of thermal inertia of buildings to tackle the frequency regulation problem. Existing DSM strategies ignore the potential service loads can provide for the Volt/Var control (VVC) problem. The ability of smart buildings to provide reactive power support to the grid has not been exploited to date. In this paper, we present a finite control state model predictive control strategy for VVC in distribution networks. The robustness of this strategy is validated on a modified IEEE 13 bus system.

Aggregation of Voltage-Controlled Devices During Distribution Network Reduction

Quasi-steady state time-series (QSTS) simulation of distribution feeders can become computationally burdensome due to many buses and devices, long simulation horizons, and/or high temporal resolution. To reduce this burden, network reduction removes buses and shifts loads/generation to the remaining buses of the circuit to produce a smaller equivalent. However, voltage-controlled devices have traditionally limited network reduction, since their operation depends on the measurement of voltage at their local bus. This work includes the reduction of buses with voltage-controlled devices by replacing the local voltage measurement with an estimate from a fast voltage sensitivity approach, which is integrated directly into a modified QSTS simulation. Comprehensive tests on an unbalanced feeder with real operating data and volt-var controlled inverters show agreement in cumulative reactive power output between the reduced and the original feeder circuits. The maximum voltage error is 0.005 Vp.u., which is nearly identical to the error in a benchmark reduction without smart inverter voltage control. The algorithm convergences for every time step, even when reducing the frequency of which the voltage estimation was updated. While the reduction methodology is demonstrated for inverter volt-var control, since it represents a frequent use case, it can be extended to other voltage-controlled devices.

Active Power Control of Grid tied PV system using Fuzzy Controller

Historically, electric power system operators have seen photovoltaic (PV) power systems as potential sources of problems due to intermittency and lack of controllability. However, the flexibility of power electronic inverters allows PV to provide grid-friendly features including volt-VAR control, ramp-rate control, high-frequency power curtailment, and event ride-through. Commercially available smart PV inverters can further provide frequency down-regulation by curtailing power, but they are unable to provide true frequency regulation through active power control (APC) because they are unable to increase power on command. This paper proposes a coordinated DC-link voltage control and deloading control for two-stage PV system to offer frequency support in an islanded microgrid without energy storage system (ESS). This paper proposes a predictive fuzzy logic based PV inverter control method for very fast and accurate control of active power

Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems

Volt-VAR control is critical to keeping distribution network voltages within allowable range, minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with incomplete and inaccurate distribution network models, we propose a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner. The Volt-VAR control problem is formulated as a constrained Markov decision process with discrete action space, and solved by our proposed constrained soft actor-critic algorithm. Our proposed reinforcement learning algorithm achieves scalability, sample efficiency, and constraint satisfaction by synergistically combining the merits of the maximum-entropy framework, the method of multiplier, a device-decoupled neural network structure, and an ordinal encoding scheme. Comprehensive numerical studies with the IEEE distribution test feeders show that our proposed algorithm outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.

Distributed Solution of Stochastic Volt/VAr Control in Radial Networks

This paper presents a fully distributed algorithm for the stochastic Volt/VAr control (VVC) problem in active distribution networks. Exact convexification of the VVC problem is achieved through the use of second-order cones on the continuous relaxation of the original optimization structure. The global optimum solution of the relaxed problem is obtained through the application of the alternating direction method of multipliers (ADMM). In order to minimize the effect of rounding off on the final solution, an adaptive threshold discretization technique is used. A two-stage control strategy is adopted where the discrete controllers, like load tap changers and switched capacitors, are dispatched at the beginning of the optimization interval and the continuous controllers, like distributed generation (DG) inverters, are adjusted in real time according to an optimized decision rule. The superiority of the proposed algorithm is demonstrated through numerical simulations on the UKGDS-95 and the IEEE-123 bus systems.