Energy Resources In Distribution Networks Research Articles

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

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.

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.

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.

A method to control distributed energy resources in distribution networks using smart meter data

Voltage regulation has been one of the major challenges for distribution network operators (DNOs) due to the integration of distributed energy resources in the recent years. Control approaches mitigating over-voltage (OV) generally require full network model, which for low-voltage distribution networks (LVDNs), can be inaccessible to DNOs. This paper presents a novel data-driven model-free control framework for improving voltage regulation in 4-wire 3-phase LVDNs. In the proposed approach, first, using smart meter data, the aggregated resistance and reactance matrices of the network are estimated. Then, these matrices serve as an input for the proposed optimal power flow (OPF) method to control the DERs’ active and reactive power outputs. This method also lets the DNO adjust the fairness in prosumers’ generation curtailment. The performance of the proposed approach is evaluated in various case studies conducted on a real-world low-voltage test feeder. The simulation results exhibit significant effectiveness in solving the OV problem associated with DERs. For example, in the studied network, the proposed approach limits the maximum voltage of the network to 1.1 pu, while without having control over the DERs output power, it can even overpass 1.17 pu during the peak generation period. In addition, utilizing DERs’ reactive control capability reduces the amount of active power curtailment, and prosumers are able to collectively inject 5.66% more active power.

Optimization of Distributed Energy Resources in Distribution Networks: Applications of Convex Optimal Power Flow Formulations in Distribution Networks

The increasing penetration of distributed energy resources (DER) has imposed several challenges in the analysis and operation distribution networks. In the last decade, the implementation of battery energy storage systems (BESS) in electric networks has caught the interest in research since the results have shown multiple positive effects when deployed optimally. However, a complex formulation of the optimization problem regarding DER implementations can easly become nonconvex, thus limiting the scope of the results (quality) and affecting the computational efficiency. In this paper, convex formulations of the optimal power flow (OPF) problem for DER (PV and BESS) installations in San Andres distribution network were implemented to minimize power losses. Four main simulation cases were stablished covering convex power flow analysis, convex optimal power flow to locate and size dispatchable and nondispatchable DER (PV) with demand and irradiation profiles, and the inclusion of aggregated and distributed PV and BESS systems. The computational realization of those formulations was made with three different software packages: CVX in python, CPLEX, and MATLAB (MATPOWER/PSO). The results show that installing DER capacity (both PV and/or BESS) can substantially improve the power losses in distribution networks, especially if considered distributed instead of aggregated, achieving reductions up to 75% for power losses and up to 57% for conventional generation utilization. Besides power losses, it was observed that optimal DER (PV and BESS) implementations behaved similarly as demand response signals do, potentially increasing economic benefits for DISCOs. In terms of computational efficiency, convex formulations help substantially to reduce computation times, especially if scalability is to be considered when distributed DER installations were studied.

Stochastic-Metaheuristic Model for Multi-Criteria Allocation of Wind Energy Resources in Distribution Network Using Improved Equilibrium Optimization Algorithm

In this paper, a stochastic-meta-heuristic model (SMM) for multi-criteria allocation of wind turbines (WT) in a distribution network is performed for minimizing the power losses, enhancing voltage profile and stability, and enhancing network reliability defined as energy not-supplied cost (ENSC) incorporating uncertainty of resource production and network demand. The proposed methodology has been implemented using the SMM, considering the uncertainty modeling of WT generation with Weibull probability distribution function (PDF) and load demand based on the normal PDF and using a new meta-heuristic method named the improved equilibrium optimization algorithm (IEOA). The traditional equilibrium optimization algorithm (EOA) is modeled by the simple dynamic equilibrium of the mass with proper composition in a control volume in which the nonlinear inertia weight reduction strategy is applied to improve the global search capability of the algorithm and prevent premature convergence. First, the problem is implemented without considering the uncertainty as a deterministic meta-heuristic model (DMM), and then the SMM is implemented considering the uncertainties. The results of DMM reveal the better capability of the IEOA method in achieving the lowest losses and the better voltage profile and stability and the higher level of the reliability in comparison with conventional EOA, particle swarm optimization (PSO), manta ray foraging optimization (MRFO) and spotted hyena optimization (SHO). The results show that in the DMM solving using the IEOA, traditional EOA, PSO, MRFO, and SHO, the ENSC is reduced from $3223.5 for the base network to $632.05, $636.90, $638.14, $635.67, and $636.18, respectively, and the losses decreased from 202.68 kW to 79.54 kW, 80.32 kW, 80.60 kW, 80.05 kW and 80.22 kW, respectively, while the network minimum voltage increased from 0.91308 p.u to 0.9588 p.u, 0.9585 p.u, 0.9584 p.u, 0.9586 p.u, and 0.9586 p.u, respectively, and the VSI improved from 26.28 p.u to 30.05 p.u, 30.03 p.u, 30.03 p.u, 30.04 p.u and 30.04 p.u; respectively. The results of the SMM showed that incorporating uncertainties increases the losses, weakens the voltage profile and stability and also reduces the network reliability. Compared to the DMM, the SMM-based problem is robust to prediction errors caused by uncertainties. Therefore, SMM based on existing uncertainties can lead to correct decision-making in the conditions of inherent-probabilistic changes in resource generation and load demand by the network operator.

Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network

Nowadays, the electrical power system has become a more complex, interconnected network that is expanding every day. Hence, the power system faces many problems such as increasing power losses, voltage deviation, line overloads, etc. The optimization of real and reactive power due to the installation of energy resources at appropriate buses can minimize the losses and improve the voltage profile, especially for congested networks. As a result, the optimal distributed generation allocation (ODGA) problem is considered a more proper tool for the processes of planning and operation of power systems due to the power grid changes expeditiously based on the type and penetration level of renewable energy sources (RESs). This paper modifies the AO using a quasi-oppositional-based learning operator to address this problem and reduce the burden on the primary grid, making the grid more resilient. To demonstrate the effectiveness of the MAO, the authors first test the algorithm performance on twenty-three competitions on evolutionary computation benchmark functions, considering different dimensions. In addition, the modified Aquila optimizer (MAO) is applied to tackle the optimal distributed generation allocation (ODGA) problem. The proposed ODGA methodology presented in this paper has a multi-objective function that comprises decreasing power loss and total voltage deviation in a distribution system while keeping the system operating and security restrictions in mind. Many publications investigated the effect of expanding the number of DGs, whereas others found out the influence of DG types. Here, this paper examines the effects of different types and capacities of DG units at the same time. The proposed approach is tested on the IEEE 33-bus in different cases with several multiple DG types, including multi-objectives. The obtained simulation results are compared to the Aquila optimizer, particle swarm optimization algorithm, and trader-inspired algorithm. According to the comparison, the suggested approach provides a superior solution for the ODGA problem with faster convergence in the DNs.

Coordination of dynamic tariff and scheduled reprofiling product for day-ahead congestion management of distribution networks

The increasing penetration of distributed energy resources in distribution networks poses new challenges to the secure and efficient operation of distribution networks. One major challenge is the network congestion caused by the non-coordinated operation of flexible demands, such as electrical vehicles and heat pumps. In general, there are two categories of market-based day-ahead congestion management methods for distribution networks: price-based method and incentive-based method. In order to use the synergy of the two types of methods and to resolve potential conflicts against regulations when one type of method is implemented solely, a coordination scheme of the two types of methods is proposed for efficient day-ahead congestion management. In the proposed coordination scheme, the dynamic tariff (DT) as a price signal is used to partly resolve congestion firstly, and the scheduled reprofiling product (SRP) as an incentive-based flexibility service product is used to deal with the remaining congestion. By employing the coordination scheme, the distribution system operator (DSO) holds the profit neutral position in terms of congestion management, denoting that the DSO does not have the congestion management cost or revenue. Based on the DT model and SRP-based model, the coordination problem is formulated as a two-level non-convex mixed-integer non-linear programming (MINLP) model that is transformed into a one-level MINLP model with linear constraints and is solved by a proposed particle swarm optimization-based solution strategy. The Roy Billinton Test System was used to conduct case studies to validate the effectiveness of the proposed coordination scheme for day-ahead congestion management in distribution networks. The case study results demonstrate that the proposed coordination scheme can efficiently resolve congestion by the coordination of the DTs and SRPs while ensuring that the DSO is in a profit neutral position.

A Review on Thermal Energy Modelling for Optimal Microgrids Management

Microgrids have become increasingly popular in recent years due to technological improvements, growing recognition of their benefits, and diminishing costs. By clustering distributed energy resources, microgrids can effectively integrate renewable energy resources in distribution networks and satisfy end-user demands, thus playing a critical role in transforming the existing power grid to a future smart grid. There are many existing research and review works on microgrids. However, the thermal energy modelling in optimal microgrid management is seldom discussed in the current literature. To address this research gap, this paper presents a detailed review on the thermal energy modelling application on the optimal energy management for microgrids. This review firstly presents microgrid characteristics. Afterwards, the existing thermal energy modeling utilized in microgrids will be discussed, including the application of a combined cooling, heating and power (CCHP) and thermal comfort model to form virtual energy storage systems. Current trial programs of thermal energy modelling for microgrid energy management are analyzed and some challenges and future research directions are discussed at the end. This paper serves as a comprehensive review to the most up-to-date thermal energy modelling applications on microgrid energy management.

A new approach for optimal allocation of photovoltaic and wind clean energy resources in distribution networks with reconfiguration considering uncertainty based on info-gap decision theory with risk aversion strategy

In this paper, a new approach for the optimal and simultaneous allocation of clean and renewable energy resources as photovoltaic panels and wind turbines and reconfiguration in radial distribution networks is presented with the aim of minimizing the cost of power losses and increasing of reliability considering generation and load the uncertainty using a new approach named information gap decision theory (IGDT) with risk aversion strategy. Decision variables include the maximum uncertainty radius of generation and load, location and size of renewable resources as well as opened network lines that are determined by satisfying the operating and radiality constraints using improved salp swarm algorithm (ISSA) based on differential evolutionary (DE) operators. The problem is implemented as deterministic and IGDT-based methods on 33 and 69 bus networks. The results indicate that the scenario of wind turbine allocation and reconfiguration in the networks simultaneously is the best scenario with the lowest cost of losses and reliability and reconfiguration only scenario is the weakest scenario with the highest cost in the deterministic and the IGDT methods. The results also showed load increasing and clean production decreasing equal to 7.58% and 34.57% for 33 bus and 4.66% and 39.88% for 69 bus network using IGDT based risk aversion for 20% uncertainty budget. The results cleared that in IGDT, the percentage of changes in the uncertain parameters is clearly and simply obtained and the network operator is able to make robust and risk aversion decision according to existing the uncertainties unlike random methods based the Monte Carlo Simulation that the results depend on the definition of the large samples.

ADMM-based market clearing and optimal flexibility bidding of distribution-level flexibility market for day-ahead congestion management of distribution networks

Nowadays, the massive deployment of distributed energy resources in distribution networks poses more operational challenges such as network congestion to distribution system operators. To cope with these challenges in an economically efficient way, the distribution system operator can utilize demand-side flexibility traded in the local flexibility market to manage the distribution network. In the existing distribution-level flexibility market frameworks, the market operator is assumed to have access to network parameters in order to ensure the market clearing solution being technically feasible from the operational point of view. To protect the privacy of network parameters, this paper proposes an alternating direction method of multipliers -based market clearing strategy, in which the market operator communicates with the distribution system operator to clear the market such that the market clearing solution respects network operation constraints without revealing network parameters to the market operator. In addition, in the existing flexibility bid formulations, the energy payback condition is determined without considering operation constraints of flexibility resources and the flexibility cost has not been considered. To fill this gap, this paper formulates an optimal flexibility bidding model for aggregators, which carefully models the energy payback condition and enables the aggregator to receive the maximum revenue with flexibility costs considered. The case studies were conducted on the Roy Billinton Test System with electrical vehicles and heat pumps as flexibility resources. The results demonstrate that the proposed distribution-level flexibility market framework is effective to perform day-ahead congestion management of distribution networks and is profitable to aggregators and end-users.

Leveraging Demand Flexibility by Exploiting Prosumer Response to Price Signals in Microgrids

The diffusion of distributed energy resources in distribution networks requires new approaches to exploit the users’ capabilities of providing ancillary services. Of particular interest will be the coordination of microgrids operating as an aggregate of demand and supply units. This work reports a model predictive control (MPC) application in microgrids for the efficient energy management of energy storage systems and photovoltaic units. The MPC minimizes the economic cost of aggregate prosumers into a prediction horizon by forecasting generation and absorption profiles. The MPC is compared in realistic conditions with a heuristic strategy that acts in a instant manner, without taking into account signals prediction. The work aims at investigating the effect that different types of energy tariffs have in enhancing the end-users’ flexibility, based on three examples of currently applied tariffs, comparing the two storage control modes. The MPC always achieves a better solution than the heuristic approach in all considered scenarios from the cost minimization point of view, with an improvement that is amplified by increasing the energy price variations between peak and off-peak periods. Furthermore, the MPC approach provides a cost saving when compared to the case considering a microgrid endowed with only photovoltaic units, in which no storage is installed. Findings in this work confirm that storage units better perform when some knowledge of future demand and supply trends is provided, ensuring an economic cost saving and an important service for the overall community.

The role of demand response in optimal sizing and siting of distribution energy resources in distribution network with time-varying load: An analytical approach

In the most studies related to allocation of distributed generation the load model assumed in constant. Such assumptions may lead to inconsistent and misleading results in the reduction of energy losses, improve system stability, etc. However, this paper is considered several different types of time-varying to allocation of units. In this study, Optimal Sizing and Siting of Distribution Generation units (Consisting of dispatchable, nondispatchable and photovoltaic) Problem are provided at the same time with considering the role of demand response and time-varying voltage-dependent load models (Consisting of commercial, industrial and residential). This method first identifies a set of buses to be placed of DG units. The optimal sizes of DG units at the identified buses are then evaluated by minimizing Multiobjective Index (Consisting of active, reactive power losses). The proposed method has been tested and validated on 33-buses test distribution system. The results show that demand response and different load models have a significant impact on the planning of Distribution Generation units in radial distribution network.

Fault Current Tracing and Identification via Machine Learning Considering Distributed Energy Resources in Distribution Networks

The growth of intermittent distributed energy sources (DERs) in distribution grids is raising many new operational challenges for utilities. One major problem is the back feed power flows from DERs that complicate state estimation for practical problems, such as detection of lower level fault currents, that cause the poor accuracy of fault current identification for power system protection. Existing artificial intelligence (AI)-based methods, such as support vector machine (SVM), are unable to detect lower level faults especially from inverter-based DERs that offer limited fault currents. To solve this problem, a current tracing method (CTM) has been proposed to model the single distribution feeder as several independent parallel connected virtual lines that traces the detailed contribution of different current sources to the power line current. Moreover, for the first time, the enhanced current information is used as the expanded feature space of SVM to significantly improve fault current detection on the power line. The proposed method is shown to be sensitive to very low level fault currents which is validated through simulations.

Concept of Designing Thermal Condition Monitoring System with ZigBee/GSM Communication Link for Distributed Energy Resources Network in Rural and Remote Applications

Monitoring the thermal behavior of distributed energy resources (DERs) network explores the dualism between thermal effects and electrical power flow. This paper proposes a design concept that monitors thermal conditions of DER grids, using ZigBee/GSM wireless sensor networks (WSNs) for real-time monitoring in rural and remote areas. The concept seeks to improve upon existing designs by integrating composite functions. The functions comprise temperature conditions monitoring, data acquisition, and wireless data transmission including data storage and abnormal conditions alert/notification for control solutions. Thus, the concept determines the thermal impact on the DERs integrated network. WSNs with temperature sensors LM35 are utilized to complement ZigBee and Global System for Mobile Communications (GSM) technologies as a communication assisted link. Temperatures are measured from solar Photovoltaic PV modules, wind turbine, distribution cables, protection control units, and energy storage facilities. The ATMEGA328P microcontroller is assigned for signal and control processing. The circuit performance is coordinated and displayed on an LCD screen for normal conditions, whereas abnormal scenarios communicate through an alert/notification by GSM Short Message Service (SMS) protocol. The development analysis was performed through algorithm and circuit simulations. Proteus software was used for circuit design. Both the algorithm and circuit analysis passed the assigned simulation stages.

Techno-economic analysis-based optimal incorporation of distributed energy resources in distribution network under load uncertainty

ABSTRACT This paper addresses optimal incorporation of various distributed energy resources (DERs), ie wind, solar and electric vehicles (EVs), in distribution system for the purpose of minimisation of total real power loss, total cost and emissions produced from grid simultaneously. To achieve these proposed objectives and benefits, a multi-objective function has been formulated. In this study, various mathematical models of DERs have been used for analysing the impact on distribution system under uncertain load demand for 24 h. In addition, the charging/discharging concept of EVs during off-peak/peak hours of the grid has also been taken into consideration. This non-linear and non-convex optimisation task has been solved through Gbest-guided Artificial Bee Colony optimisation technique because of its robustness. However, from the obtained numerical outcomes through the proposed technique, it is observed that the integration of DERs is capable of reducing total real power loss, total cost and emissions to a great extent.

Peer-to-Peer Energy Trading among Microgrids with Multidimensional Willingness

Networked microgrids are emerging for coordinating distributed energy resources in distribution networks in the future Energy Internet, for which developing an efficient energy market model is crucial for facilitating multi-directional trading among microgrids. In this paper, a peer-to-peer energy trading mechanism is presented using non-cooperative bidding among microgrids. Multidimensional willingness, including time pressure and counter behavior for mimicking the personalized behaviors of microgrids, was taken into account in the design of the bidding strategy. Under a parallel trading framework based on a blockchain, the proposed multidimensional willingness bidding strategy turns out to be able to make rational decisions with sufficient flexibility in the bidding process. The simulation results of a realistic case of microgrids from Guizhou Province, China, validate that the proposed peer-to-peer energy trading mechanism is capable of raising the microgrids’ profits and renewable energy source utilization.

Distribution network planning considering technology diffusion dynamics and spatial net-load behavior

This paper presents a data-driven spatial net-load forecasting model that is applied to the distribution network expansion problem. The model uses population census data with Information Theory-based Feature Selection to predict spatial adoption patterns of residential electric vehicle chargers and photovoltaic modules. Results are high-resolution maps (0.02 km2) that allow distribution network planners to forecast asymmetric changes in load patterns and assess resulting impacts on installed HV/MV substation transformers in distribution systems. A risk analysis routine identifies the investment that minimizes the maximum regret function for a 15-year planning horizon. One of the outcomes from this study shows that traditional approaches to allocate distributed energy resources in distribution networks underestimate the impact of adopting EV and PV on the grid. The comparison of different allocation methods with the presented diffusion model suggests that using conventional approaches might result in strong underinvestment in capacity expansion during early uptake and overinvestment in later diffusion stages.