Network Of Distributed Resources Research Articles

Wide-Area Protection System for Radial Smart Distribution Networks

The integration of Distributed Energy Resources (DERs) in power distribution networks poses challenges for protection systems due to dynamic bidirectional fault currents. This paper presents a novel wide-area protection scheme for modern Doubly Feed Induction Generator (DFIG)-integrated distribution networks that simplifies fault location and relay coordination. The system employs Artificial Neural Networks (ANNs) for fault detection and a Fault Zone Relay Condition (FZRC) matrix for fault location. The simulation results demonstrate the effectiveness of the proposed approach in fault detection and isolation.

Integration flexibility of renewable distributed energy resources in active distribution networks: A two-module data-driven characterization method

Characterizing the integration flexibility of renewable distributed energy resources (RDERs), which describes the feasible region of RDER accommodations with which the operational constraints of an active distribution network (ADN) can be respected, facilitates the AND operations. However, traditional characterization methods face the challenge of concurrently dealing with inevitable AC power flow and numerous RDERs. To resolve this issue, this paper develops a novel characterization method that contains two interconnected modules: learning and updating modules. The two modules jointly and iteratively calculate the parameters to characterize the integration flexibility of RDERs (IFR). In the learning module, a new loss function is designed to train the IFR parameters using a training data set. In the updating module, two optimization problems are developed based on the optimization-based performance estimation of the trained parameters to generate more data points for performance improvement in the learning module. The effectiveness of the proposed method is corroborated in the IEEE 33-bus and IEEE 136-bus test systems.

Centralized and decentralized combined voltage control for distribution network with high penetration PV connected

With the high penetration of distributed photovoltaics (DPVs) integrated into the distribution network, the voltage problem becomes a critical problem. At present, the resources in the current distribution network hardly satisfy the requirements of voltage regulation. However, the huge amount of DPVs in the distribution network could be a potential voltage regulation resource due to the controllable active power and reactive power. In this paper, a day-ahead centralized and intraday decentralized combined voltage control method is proposed for high penetration DPVs connected distribution Networks. In the part of day-ahead control, centralized compensation of the distribution network voltage is achieved by controlling the gear ratio of the capacitor/reactor group on an hourly scale. The intraday control part consists of decentralized local control based on DPVs and centralized control based on energy storage (ES) equipment. The reactive and active power of DPVs is controlled in a decentralized way by using local priority sensitivity control based on the coordination with capacitor optimization. Finally, simulations are performed to verify the performance of the method. The results indicate that this method can effectively solve the voltage problem caused by DPVs.

Multi-Objective Battery Coordination in Distribution Networks to Simultaneously Minimize CO2 Emissions and Energy Losses

The techno–environmental analysis of distributed energy resources in electrical distribution networks is a complex optimization task due to the non-convexities of its nonlinear programming formulation. This research employs convex optimization to address this issue while minimizing the expected carbon dioxide emissions and daily energy losses of a distribution grid via the optimal dispatch of battery energy storage units (BESUs) and renewable energy units (REUs). The exact non-convex model is approximated via semi-definite programming in the complex variable domain. The optimal Pareto front is constructed using a weighting-based optimization approach. Numerical results using an IEEE 69-bus grid confirm the effectiveness of our proposal when considering unitary and variable power factor operation for the BESUs and the REUs. All numerical simulations were carried out using MATLAB software (version 2022b), a convex disciplined tool (CVX), and the semi-definite programming solvers SEDEUMI and SDPT3.

A joint configuration method for multiple flexible resources in low carbon distribution networks based on massive scene dimensionality reduction

Abstract Due to the extensive access of renewable sources in distribution networks, this research proposes a joint configuration method for multiple flexible resources in low carbon distribution networks based on massive scene dimensionality reduction to maximize the access capacity of renewable sources. The dimensionality reduction clustering is carried out on the wind-light-load mass high-dimensional scenes by the principal component Gaussian mixture clustering algorithm, and the typical scene set of wind-power-loads is obtained. Hereafter, a joint configuration model for multiple flexible resources of distribution network for massive scenarios is constructed, and using the second-order cone relaxation technique to convert the non-convex constraints in the model to convex processing. Last, a Portugal 54 distribution system is employed to verify the proposed method.

Multiobjective and Coordinated Reconfiguration and Allocation of Photovoltaic Energy Resources in Distribution Networks Using Improved Clouded Leopard Optimization Algorithm

In this paper, a multiobjective framework for simultaneous reconfiguration and allocation of photovoltaic (PV) energy resources in radial distribution networks is performed for minimizing the power losses, lowering network loading factor, and reducing cost of energy resources as well as increasing the voltage stability index. A novel algorithm called the improved clouded leopard algorithm is used to find the set of optimum decision factors in the combined execution of restructuring and also PV resource distribution. The clouded leopard optimization (CLO) algorithm, which is widely used, takes its cues from the sleeping and foraging habits of the animal, and the improved CLO (ICLO) is formed using adaptive inertia weight to overcome premature convergence. In five cases of base distribution network and different contribution of reconfiguration and PV allocation, single- and multiobjective approach has been applied on 33- and 69-bus distribution networks. According to the findings, the best case includes simultaneous reconfiguration and PV allocation in the radial networks based on the multiobjective approach unlike the single-objective method which obtained the highest network performance with the best compromise between different goals. Also, in the best lowest losses, the decrease in the network loading and cost of energy resources and the better voltage profile and stability are obtained satisfying the constraints. The losses, network loading, voltage profile, and voltage stability are improved by 60.40%, 37.89%, 6.17%, and 27.5% for 33-bus network and also enhanced by 71.77%, 41.23%, 4.76%, and 20.48% for 69-bus network, respectively. Also, the results showed that network reconfiguration only has the weakest performance among other cases based on reconfiguration or photovoltaic sources. Moreover, the superior capability of the ICLO in addressing the aim of the study is proved in comparison with the traditional CLO and well-known particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) to obtain the better objective value and statistical criteria to solve the best case.

On Integrating and Operating Distributed Energy Resources in Distribution Networks: A Review of Current Solution Methods, Challenges, and Opportunities

On Integrating and Operating Distributed Energy Resources in Distribution Networks: A Review of Current Solution Methods, Challenges, and Opportunities

Automated Correction of GIS Data for Loads and Distributed Energy Resources in Secondary Distribution Networks

Currently, utilities are making significant efforts to correct their secondary models in geographic information systems (GISs) to capture the distribution system performance due to the increased penetration of distributed energy resources (DERs) such as renewable energy resources, distributed energy storage, and electric vehicles (EVs). However, their databases are often not reliable in terms of GIS coordinates of elements such as loads and DERs resulting in an approximate representation of the secondary distribution network. This paper presents an automated tool for accurate secondary network topology GIS coordinates correction. The proposed automated tool provides a more accurate feeder topology for utilities to estimate and operate distribution systems by assigning the load and DER nodes to their corresponding customer location. To simplify the complexity of the proposed new tool, only two commonly available inputs are being used as input data: municipal parcel GIS delimitation data, and utility secondary feeder topology database. A three-stage framework is proposed to develop the automated tool: the first stage reads and processes the raw input data; the second stage works automatically with no human intervention to assign the load and DER nodes to their associated location; the third stage provides the load and DER coordinates and physical address. The proposed procedure is implemented on an actual feeder from an electrical utility with high penetration of DERs.The results show that the proposed method provides a more accurate estimation of the topology of the feeder in terms of coordinates.

Joint Planning of Utility-Owned Distributed Energy Resources in an Unbalanced Active Distribution Network Considering Asset Health Degradation

Joint Planning of Utility-Owned Distributed Energy Resources in an Unbalanced Active Distribution Network Considering Asset Health Degradation

Comparison of network-(in)secure bidding strategies to coordinate distributed energy resources in distribution networks

Aggregators are acknowledged as key agents to enable the active participation of household and commercial distributed energy resources (DER) in electricity markets. In recent years, many researchers and practitioners have been working on the development of diverse network-secure and network-insecure bidding strategies to support the participation of DER aggregators in electricity markets. An example of this is the extensive work developed by the authors of this paper in various R&D projects with industry. This paper builds on the experience gained in previous works and its main contribution is a thorough comparison between these strategies, resulting in an extensive discussion of their pros and cons. The discussion compares the economic and network security performance of the strategies, as well as their communication, computational, and data privacy requirements. To discuss and quantify these aspects, we formulate, implement, and test various bidding strategies on a real-world MV-LV distribution network with 2 aggregators and 522 customers for multiple DER scenarios. The discussion of the results provides realistic and valuable information on the pros and cons of each strategy, helping energy system stakeholders to understand which strategy may better fit their needs.

Agregador de potencia en redes de distribución activas usando IoT

This paper presents an Internet of Things (IoT) architecture for a power aggregator of energy resources in active distribution networks. Two types of algorithms are evaluated and compared: centralized and decentralized control. The former is based on real-time estimation of the demand and subsequent optimization. The latter is based on the Alternating Direction Method of Multipliers (ADMM). Both algorithms were evaluated on an IoT platform consisting of agents implemented on a series of small single-board computers based on Raspberry Pi technology, connected to a centralized computer that emulates the grid. This platform allows realistic evaluation of the algorithms, considering the effects of communication. The main grid considers power losses and the dynamics of inverter-based renewable resources using quasi-dynamic simulation. This type of simulation can be considered real-time for this application. The platform is demonstrated to be flexible and provides a real view of the practical problems that aggregators may face when implementing them in a power distribution network.

Coordinated Optimization of Emergency Response Resources in Transportation-Power Distribution Networks Under Extreme Events

The proliferation of electric vehicles (EVs) and the increasing interdependence across power distribution networks (DNs) and transportation networks (TNs) have increased the complexity and vulnerability of the two systems in extreme circumstances. As the interdependence of two infrastructures tightens over time, it is viewed as a dire necessity to strengthen the resilience of the coordinated transportation-power distribution networks (TDNs) against natural disasters. This paper constructs a coordinated optimization method of TN traffic link reversing, DN line switching, and fast charging pile management, to improve the TDN performance in the emergency response stage after disasters. A dynamic TN model and a multi-period DN model are integrated in TDN modeling to capture flow propagations and state variations among time intervals. The coordinated optimization for TDN emergency response is designed as a mixed-integer nonlinear programming (MINLP) problem with high-order objective functions and nonlinear constraints to minimize TN travel costs and DN active and reactive power shortages. An accuracy-aware adaptive piecewise linearization approach combined with Gray code-based encoding is utilized to improve the computational efficiency for solving the TDN optimization problem. Numerical simulations show that the TDN performance is enhanced by coordinating various DN and TN resources, as compared with those of separate and conventional topology controls. The proposed TDN solution method has significantly reduced the computation time for managing extreme conditions while guaranteeing the accuracy of the results as compared with those of the nonlinear model and the linearized model by uniform piecewise linearization.

Communication-less adaptive directional overcurrent protection strategy considering islanded mode detection in active distribution networks

The integration of distributed energy resources (DERs) in electric distribution networks originates the Active Distribution Networks (ADNs). The presence of DERs introduces challenges to conventional protection schemes due to bidirectional power flows and variations in fault current magnitudes; furthermore, the connection and disconnection of DERs result in multiple ADN operating modes (OMs), deteriorating the effective operation of protective relays. Considering the above, this document presents an adaptive protection strategy to address the challenges related to the variable nature of the ADN operation. The main contribution of the proposed approach is accurate islanded mode detection without requiring communication links, enabling fault detection in all ADN operating modes. The protection strategy maintains directional overcurrent relays' coordination using local voltage and current measurements to continuously estimate the Thevenin equivalent, fault currents, and adaptive coefficients.Relay coordination is maintained through tests conducted on a modified IEEE 34-bus feeder, considering several OMs, fault types, and locations. A comparison between the proposed adaptive protection strategy and a conventional overcurrent scheme shows that relay operating time is better in 100% of the analysed cases for the former strategy. On the other hand, the maximum errors in the fault current estimation using the Thevenin equivalent parameters are between 0.05% to 2.6% for all of the analysed operating modes when compared with the true fault current magnitudes. Finally, the adequate results validate the potential of the proposed communication-less protection strategy for modern protective relaying applications.

A High-Power Density DC Converter for Medium-Voltage DC Distribution Networks

A DC converter is the core equipment of voltage conversion and power distribution in a DC distribution network. Its operating characteristics have a profound impact on the flexible regulation of distributed resources in an active distribution network. It is challenging for the existing single-stage conversion topology to meet the requirements of distributed renewable energy connected to a multi-voltage level, medium-voltage grid. It is necessary to study the multistage transform power unit topology further, which can satisfy high reliability, high efficiency, and wide input range. This paper proposes a high-power density DC converter for medium-voltage DC networks with wide voltage levels. It adopts Buck-LLC integrated modular composition. The input ends of the high isolation resonant power unit are connected in series to provide high voltage endurance, and the output ends are connected in parallel to meet the high-power demand and achieve high-power transmission efficiency. The proposed series dual Buck-LLC resonant power unit topology can adjust the duty cycle of series dual buck circuits to meet the needs of different levels of medium-voltage DC power grids. The soft switching problem within the wide input range of all switching tubes is solved by introducing auxiliary inductors, thereby improving energy transmission efficiency. The auxiliary circuit and control parameters are optimized based on the research of each switching tube’s soft switching boundary conditions. Finally, an experimental prototype of a 6.25~7 kW power unit is designed and developed to prove the proposed topology’s feasibility and effectiveness. Great breakthroughs have been made both in theoretical research and engineering prototype development.

Adaptive primal–dual control for distributed energy resource management

With the increased adoption of distributed energy resources (DERs) in distribution networks, their coordinated control with a DER management system (DERMS) that provides grid services (e.g., voltage regulation, virtual power plant) is becoming more necessary. One particular type of DERMS using primal–dual control has recently been found to be very effective at providing multiple grid services among an aggregation of DERs; however, the main parameter, the primal–dual step size, must be manually tuned for the DERMS to be effective, which can take a considerable amount of engineering time and labor. To this end, we design a simple method that self-tunes the step size(s) and adapts it to changing system conditions. Additionally, it gives the DER management operator the ability to prioritize among possibly competing grid services. We evaluate the automatic tuning method on a simulation model of a real-world feeder in Colorado with data obtained from an electric utility. Through a variety of scenarios, we demonstrate that the DERMS with automatically and adaptively tuned step sizes provides higher-quality grid services than a DERMS with a manually tuned step size.

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. This paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

Modeling and characterization of the impact of capacity limitation services on distribution networks

Various congestion management strategies have been proposed to tackle the operational challenges arising from the widespread integration of distributed energy resources (DERs) in distribution networks. Among these strategies, capacity limitation services have recently gained significant attention. However, comprehensive analyses are essential for system operators to fully understand the impact and effectiveness of these services in distribution networks. In this study, we develop a model to represent the aggregator’s response to capacity limitation services and assess their impact on network states. The proposed model, from the aggregator’s perspective, consists of non-flexible baseload and controllable DERs, optimizing the aggregator’s benefits based on current market conditions without accessing network states. Consequently, system operators must ensure distribution network security while facilitating flexible resources. Given the unavailability of customer privacy and the complexity of multi-period consumption behaviors, system operators concentrate on capturing aggregator behaviors during potentially risky congestion periods. Our analysis specifically aims to quantify the effects of service area coverage, power capacity limitation values, and service durations. We establish an evaluation framework to coordinate various modules by assessing these services under different scenarios. Simulation results demonstrate that the proposed model can effectively represent power consumption behavior using partially accessible information. Furthermore, we design an associated procedure to offer distribution system operators valuable insights into system behavior, empowering them to request appropriate services in anticipation of potential operational issues.

Convex Stochastic Approaches for the Optimal Allocation of Distributed Energy Resources in AC Distribution Networks with Measurements Fitted to a Continuous Probability Distribution Function

This paper addresses the optimal stochastic allocation of distributed energy resources in distribution networks. Typically, uncertain problems are analyzed in multistage formulations, including case generation routines, resulting in computationally exhaustive programs. In this article, two probabilistic approaches are proposed–range probability optimization (RPO) and value probability optimization (VPO)–resulting in a single-stage, convex, stochastic optimal power flow problem. RPO maximizes probabilities within a range of uncertainty, whilst VPO optimizes the values of random variables and maximizes their probabilities. Random variables were modeled with hourly measurements fitted to the logistic distribution. These formulations were tested on two systems and compared against the deterministic case built from expected values. The results indicate that assuming deterministic conditions ends in highly underestimated losses. RPO showed that by including ±10% uncertainty, losses can be increased up to 40% with up to −72% photovoltaic capacity, depending on the system, whereas VPO resulted in up to 85% increases in power losses despite PV installations, with 20% greater probabilities on average. By implementing any of the proposed approaches, it was possible to obtain more probable upper envelopes in the objective, avoiding case generation stages and heuristic methods.

On the applicability of single-line equivalents on optimal operation of modern unbalanced distribution networks

The integration of Distributed Energy Resources (DERs) in distribution networks comes with challenges, like power quality concerns, but also opens up new opportunities, e.g., DERs can offer competitive energy prices for final users by leveraging time arbitrage. A suitable method to fully exploit such opportunities is to compute the optimal DER schedule, either with a full three-phase network model or a more computationally efficient single-line equivalent. This paper presents under which conditions a single-line equivalent can and cannot be used to properly represent a modern and unbalanced power distribution network able to dispatch high levels of DER integration optimally. Results show that single-line equivalents might be helpful when the problem objective function limits counterflows, for example, when minimizing active power losses. Moreover, single-line equivalents might be helpful for low levels of DER integration. However, enabling single-line equivalents results in a lower hosting capacity for high levels of DER integration.

Mobile energy storage systems with spatial–temporal flexibility for post-disaster recovery of power distribution systems: A bilevel optimization approach

In recent years, the damage to power distribution systems caused by the frequent occurrence of extreme disasters in the world cannot be ignored. In the face of the customer’s demand for high power supply reliability and high power quality, it is urgent to establish a resilient distribution network that can not only resist extreme disasters and quickly recover the power distribution system loads, but also ensure a high voltage quality of the distribution system during recovery process. Therefore, mobile energy storage systems with adequate spatial–temporal flexibility are added, and work in coordination with resources in an active distribution network and repair teams to establish a bilevel optimization model. The objective of the upper-level optimization model is minimum the total load curtailment of the distribution system after the disaster. And the objective of the lower-level optimization model is minimum the voltage offset of power supply buses during the recovery stage. A modified IEEE 33-bus distribution test system is used to verify this model. The simulation results show that the bilevel optimization strategy proposed in this research can effectively reduce the voltage offset during the recovery process of distribution systems, and ensure the minimum total load curtailment simultaneously.