Operation Of Distributed Energy Resources Research Articles

Optimization of Distributed Energy Resources Operation in Green Buildings Environment.

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA-GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler.

An SDP relaxation in the complex domain for the efficient coordination of BESS and DGs in single-phase distribution grids while considering reactive power capabilities

This research proposes an efficient energy management system design for medium-voltage distribution networks from a perspective of convex optimization. The exact nonlinear programming problem that represents the operation of distributed energy resources (DERs), such as batteries and photovoltaic sources, is approximated as an equivalent semi-definite programming (SDP) problem. This SDP approximation is achieved in the complex domain using Hermitian matrices, which are the equivalent of positive semi-definite matrices in the real number domain. Two test feeders, one with 27 nodes and the other with 33 nodes, are modified to match the characteristics of Colombian rural and urban networks. The 27-bus grid is adapted to accommodate the average demand and solar generation conditions of the municipality of Capurganá, while those of the city of Medellín are used for the 33-bus grid. One of the main contributions of this research is that it considers a DER operation with variable power factors, which allows for significant improvements compared to the traditional use of a unitary power factor scheme. Numerical comparisons are conducted using metaheuristic optimizers, including parallel versions of the particle swarm optimizer, the vortex search algorithm, and the continuous version of the genetic algorithm. These comparisons demonstrate the effectiveness of the proposed SDP approximation. All simulations are carried out in the MATLAB software (version 2022b), using the disciplined convex optimizer (CVX) and the SDPT3 and SEDUMI solvers.

Regression based stochastic energy management strategy for a grid connected microgrid using Artificial Electric Field Algorithm

Recent decade has witnessed steep technological advancement in the renewable energy sector due to growing concern of climate change and emission cut target. The lack of availability of fossil fuels and the high price of the same have made the use of Renewable Energy Sources (RES) as a promising approach for generating clean and sustainable energy at local level. This persuades the implementation of Microgrid (MG) supported with RES, to cater various types of load demand. However, planning and optimizing a MG operation have become challenging due to the intermittent nature of RES and uncertain loads. To address this issue, this paper introduces a Stochastic Energy Management Strategy (SEMS) for a grid connected MG comprising Photovoltaic, Wind Turbine, Fuel Cell, Micro Turbine, Battery Energy Storage and electrical as well as heat energy demand. The volatilities of RES power output and energy demand are modeled via Gaussian Process Regression (GPR) and Monte Carlo Simulation (MCS) respectively. The problem is framed as non-linear (NL) dynamic stochastic optimization problem and solved using ‘Artificial Electric Field Algorithm (AEFA)’. Here, minimization of expected value of operational and emission cost are taken as objective functions; while various practicality aspects are modeled as constraints in the problem. Further, the efficacy of the proposed approach is validated around the first central moment of the load. Various case studies are performed to estimate the day ahead optimum operating schedule of the Distributed Energy Resources (DER) and to assess the impact of load uncertainties on DER operation and total MG operation cost. Further, the efficacy of the GPR is verified through comparison with MCS based result.

Cyber–physical event reasoning for distributed energy resources

The widespread adoption of internet-connected and remotely controllable solar plants and energy storages renders coordinated cyber–physical attacks against distributed energy resources (DERs) an emerging risk for power systems. Effective incident response can be facilitated by online DER monitoring providing real-time information on event root causes and physical impacts. Such online event identification is challenged by the lack of historical attack observations, and emergence of new attack strategies. The Cyber-Physical Event Reasoning System CyPhERS provides real-time information on both known and unknown attack types in form of informative and interpretable event signatures, without need to be trained on historical attack samples. To date, CyPhERS has only been demonstrated on a laboratory water distribution testbed of limited complexity, considering human evaluation of event signatures. This work methodologically adapts CyPhERS to specificities of DER operation such as weather and consumer-induced volatility, and introduces an automated signature evaluation system. The feasibility of applying CyPhERS for automated DER monitoring is investigated on a dataset recorded from a real photovoltaic-battery system targeted by several cyber and cyber–physical attack types. The results demonstrate that the proposed methodological adaptations and signature evaluation system enable the application of CyPhERS for automated online identification of different attack types targeting DERs, while greatly reducing the false positive rate.

Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability

The virtual power plant (VPP) provides an effective way for the coordinated and optimized operation of distributed energy resources (DERs). To solve the aggregation problem of a VPP containing scattered layouts and heterogeneous performance DERs, this study proposes a dynamic aggregation strategy to improve the flexible regulation ability of the VPP. A VPP aggregation model considering network constraints and temporal coupling constraints of DERs is constructed, while some VPP performance parameters are proposed to characterize and quantify the regulation ability. An uncertainty set considering time-series coupling properties of variables is constructed, and an aggregation method under uncertainty scenarios is proposed based on a two-stage robust optimization model. In addition, a dynamic aggregation strategy is proposed for the application of VPPs in electricity markets. Finally, case studies demonstrates that the proposed method provides a more extensive feasible region compared to other methods, and the deviation power remains within a reasonable range. The dynamic aggregation strategy facilitates the synergistic and correlative operation of DERs, exploits the regulation ability of the VPP in frequency regulation and spinning reserve, and improves the feasibility of practical applications. Simultaneously, the income of the VPP increased by 29.5 %.

Investment in vehicle-to-grid and distributed energy resources: Distributor versus prosumer perspectives and the impact of rate structures

Photovoltaic panels, electric vehicles, and vehicle-to-grid technologies are becoming more common and hold significant promises to improve the grid and foster the energy transition. However, significant questions remain unanswered with respect to who should invest in this equipment and what tariff should be used. This paper examines whether the distribution company or prosumer should invest in and manage Distributed Energy Resources (DER), the ideal combination of DER to utilize, and the appropriate tariff to implement. Central to this analysis is the assessment of different stakeholder objectives, particularly from the investor's perspective, where net present value is used as the primary criterion for evaluating the different investment scenarios. Additionally, the impact of these scenarios on the annual system cost is calculated. A mathematical scenario analysis model is developed to simulate the operation of DER and energy management systems. This model utilizes the Vermont electricity grid's real-world consumption, generation data, and cost structures. The results underscore the significance of incorporating vehicle-to-grid technology to enhance the profitability of DER investments. This inclusion of specific data sources and stakeholder criteria aims to provide insight into the complex dynamics of smart-home deployment.

Coordinated, Centralized, and Simultaneous Control of Fast Charging Stations and Distributed Energy Resources

The growing penetration of fast charging stations (FCSs) to electric vehicles (EVs) and distributed energy resources (DERs) in the electrical power system brings technical issue changes in the voltage profile throughout grid nodes and feeder current overload. The provision of ancillary services by DERs and FCSs arises as an appealing solution to reduce these adverse effects, enhancing the grid hosting capacity. The control of microgrids is essential for the coordinated implementation of these services. Although microgrid control is widely applied to DERs, few studies address the coordinated control of DERs and FCSs to obtain benefits for the electrical power system. This paper proposes a coordinated and simultaneous control of DERs and FCSs based on the power-based control (PBC) strategy, efficiently exploiting FCSs in a microgrid model previously unaddressed in the literature. The results show that, with the coordinated control of DERs and FCSs, the control of the power flow in a minigrid (MG) is achieved both in moments of high generation and in moments of high load, even with the maximum operation of DERs. This method allows for the maintenance of voltage levels within values considered acceptable by technical standards (above 0.93 pu). The maintenance of voltage levels is derived from reducing the overload on the point of common coupling (PCC) of the minigrid by 28%, performing the peak shaving ancillary service. Furthermore, the method allows for the control of zero power flow in the PCC of the minigrid with the upstream electric grid in periods of high generation, performing the ancillary service of valley filling. The method performs this control without compromising vehicle recharging and power dispatch by DERs.

Cooperative Operation of Distributed Energy Resources and Thermal Power Plant With a Carbon-Capture-Utilization-and-Storage System

Carbon-capture-utilization-and-storage (CCUS) system plays a critical role in the process of decarbonization. This paper proposes a cooperative operation model for a CCUS-based thermal power plant and distributed energy resources. The critical purpose is to achieve a higher profit and flexibility together with a lower carbon emission for the CCUS system under the electricity, carbon, ancillary services, and synthesis fuel markets. First, to enhance the operational flexibility in various markets, a critical focus of this paper is on elaborating feasible operation domain of the CCUS system through identifying the physicochemical processes associated with carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) treatment and multi-energy conversion. Second, a Nash-bargaining framework is developed to describe how the CCUS works with distributed energy resources cooperatively to consume cheap and clean renewables to treat CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Third, to solve the cooperative operation problem efficiently and accurately, an adaptive sequential cone programming (ASCP) method together with a progressive hedging variant of the Benders decomposition (PH-BD) algorithm are designed. Case studies indicate the total revenue of the thermal power plant under the cooperative mode is improved by up to 8.85% and 66.53% compared with the individual mode with and without the CCUS. Further, the solution quality and efficiency of proposed methods are verified.

Enhancing resilience by outage management strategy based on semidefinite programming relaxation method in unbalanced microgrids

During electrical power outages caused by extreme events, microgrid operators (MGOs) attempt to restore as much electrical load as possible. This work suggests a strategy for outage management (OM) to improve microgrid resilience by using two optimal actions: distribution feeder reconfiguration (DFR) and scheduling of distributed energy resources (DERs). After a line fault, the radial network topology is determined by the proposed model using the rank of the incidence matrix. The proposed model is formulated by an analytical optimization method created by semidefinite programming (SDP) relaxation. The SDP changes the non-convex and nonlinear model suggested for OM into an approximated convex model, which commercial software applies. The proposed model considers the uncertain behavior of non-dispatchable DERs and the electrical demands that deal with an information gap decision theory (IGDT) based on a risk-averse strategy. To improve the proposed model's flexibility, the shortened electrical demand as a demand response program (DRP) is considered. The aim of optimization is the minimization of the accumulative cost for dispatchable DER operation and load reduction. The suggested SDP model is figured out using the MOSEK solver in GAMS software. Using the offered model in the 69-bus unbalanced test system displays that the SDP model averagely decreases total operation cost and execution time by 1.04% and 61.29% on all scenarios in comparison with the conventional GAMS model.

Detecting and classifying man-in-the-middle attacks in the private area network of smart grids

The sustainable development of smart grids requires the massive deployment of renewable energy, in a highly distributed manner, introducing new challenges for the system operation. Therefore, the integration of information and communication technologies in sites with Distributed Energy Resources (DERs) is needed to monitor and control the DERs operation. In this scheme, a local controller is installed at each DER site to interact with the centralized applications at the grid level and the power equipment at the site level. This local controller uses client–server protocols (e.g., Modbus TCP/IP and IEC 61850 Manufacturing Message Specification) to communicate with different power equipment in the Private Area Network (PAN) of the site. Such protocols often lack information confidentiality and integrity mechanisms. As a result, the smart grids become vulnerable to cyber-attacks. To safeguard smart grid applications, this paper proposes a Hybrid Network Intrusion Detection System approach (HNIDS), where machine learning-based anomaly and signature-based are combined. The proposed methodology detects and classifies Man-In-The-Middle (MITM) attacks in eavesdropping mode in PANs, without violating customer privacy. The ability to detect unknown MITM attack techniques, identify affected packets, and determine the victim device(s) are the major advantages of this approach. An experimental testbed has been used to collect real-life data and validate the effectiveness of the proposed approach in smart grid applications. The proposed HNIDS is evaluated using a simulation as well as real-life laboratory experiments, demonstrating very high accuracy in detection rate, from 97.6% to 100%, with an average of the weighted F1-score over 98%.

Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation

In this paper, a distributed online prediction optimization algorithm is proposed to optimize the operation of distributed energy resources (DERs) considering multi-period constraints. First, we build a time-varying operation optimization model of DERs in multi-area distribution networks considering the operation constraints among different periods (e.g., the relationship of DERs energy within a certain amount of time). Second, we design an online prediction algorithm to solve the time-varying model in a distributed way. Specifically, it decouples the sensitivities of power flow states among different distribution areas. Based on this, a model is set up to characterize the mapping relationship among power flow states and the increments of DER output powers among different periods, and to predict the power flow states in the future only depending on the local and some aggregated information of each area. Thus, the solution of the time-varying optimization problem is decomposed into solving several subproblems in a distributed way. Finally, the proposed algorithm is verified in a 502-node distribution system. It achieves the optimal operation of DERs with satisfying the constraints of the energy plan formulated to reserve enough energy for the regulation in future periods. Compared with the centralized method, the proposed algorithm achieves similar optimization results with significant reduction of calculation time.

Load Factor Improvement of the Electricity Grid Considering Distributed Energy Resources Operation and Regulation of Peak Load

In recent decades, in urban centers, several services (such as health, food, security, connectivity, etc.) have become more dependent on electricity. With the population increase, the energy requirements of these services can lead the electricity network (EN) to a state of operational stress. In this scenario, distribution companies (DISCOs) need to develop efficient energy management strategies to avoid wasting electricity in its EN. The load factor (LF) of an EN is an indicator related to efficient electricity usage within a time horizon (e.g., day, month, year, etc.). LF improvement can be achieved through an efficient operational scheme that combines several strategies. Among them, peak load regulation in the demand profile of all the consumers, together with the operation of distributed energy resources (DERs), such as renewable energy sources (RESs) and capacitor banks (CBs), represents a promising alternative. In order to address this efficient scheme, this work proposes a mixed-integer linear programming (MILP) model that aims to increase the LF through the bulk scheduling of residential, commercial, and industrial electrical appliances during the day, considering a baseline demand and an hourly rate. At the same time, voltage fluctuations and technical losses are mitigated by the presence of DERs in the EN. Operational constraints related to the performance of EN, RESs, and CBs are considered. Uncertainties in the habitual demand profiles and solar irradiance during the day are represented through simulation algorithms. In order to evaluate the proposed model, a modified 14-node IEEE EN was used. The results corroborate the applicability of this proposed MILP model, reducing electricity waste and ensuring an efficient EN operation.

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high <inline-formula> <tex-math notation="LaTeX">$R/X$</tex-math> </inline-formula> ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.

Optimal Coordination for Multiple Network-Constrained VPPs via Multi-Agent Deep Reinforcement Learning

This paper proposes a multi-agent deep reinforcement learning method to coordinate multiple microgrids owned virtual power plants (VPPs) connected in the active distribution network. A communication-based independent twin delayed deep deterministic policy gradient method is proposed to implement a partially observable Markov game for coordinating multiple VPPs (agents). The decentralized training process can protect the privacy of the agent-based entity participating in the framework, and each entity can share equal rights with simultaneous decision-making. Internal markets are proposed in our framework that the price contains the information of energy trading and network information. The price quota curve based external market is proposed for further exploiting distributed energy resource operation potential to enable VPPs with market power to be price-makers. The proposed method has been verified in a modified IEEE test system to prove its effectiveness. The results confirm that the proposed framework can simultaneously improve the operation cost and maintain network operational safety.

Peer-to-peer electricity trading considering voltage-constrained adjustment and loss allocation in blockchain-enabled distribution network

Interest in peer-to-peer (P2P) electricity trading has been growing, especially with respect to distributed energy resources (DERs) accommodation in distribution networks, which utilizes P2P trading mechanism to realize the decentralized operation of DERs. A key technology underpinning P2P electricity trading is the blockchain, which ensures that P2P trading is efficient and transparent. However, due to the limited calculation performance of blockchain, the blockchain-based P2P trading process has to be simplified, which makes on-chain grid security-constrained trading adjustment and on-chain power loss allocation become difficult to be solved. To address this challenge, a new on-chain P2P electricity trading framework with easy implementation is proposed. First, an on-chain linear power flow (OC-LPF) model is developed to reduce the difficulty of on-chain power flow calculation. Second, a novel self-adjustment loss allocation model with low complexity is proposed, which integrates the power loss into OC-LPF-based virtual power variable, and enables power flow calculation and power loss allocation to be performed synchronously. Third, a distributed market clearing algorithm with accelerated convergence based on the OC-LPF model is designed to improve the efficiency of on-chain convergence while protecting the privacy of participants. In addition, the effectiveness of the proposed mechanism has been verified with simulation studies on Ethereum.

Economic scheduling of virtual power plant in day-ahead and real-time markets considering uncertainties in electrical parameters

The virtual power plant (VPP) concept, which offers energy efficiency, benefits significantly from the tremendous potential of integrating distributed energy resources (DERs). This work proposes optimal economic dispatch of virtual power plant (VPP), and the problem is formulated in a novel way considering the potential of energy storage systems (ESS) of electric vehicles (EVs) and data center (DC) in a data center facility test model using optimized energy management system (EMS). The problem is evaluated for maximizing the revenue for VPP considering market prices and the uncertainties in the distributed energy resources. To this purpose, a techno-economic analysis of the proposed test system of VPP is evaluated using mixed-integer linear programming (MILP). Renewable generation, electricity price, load demand, and real-time EV behavior uncertainty analysis are forecasted using Long Short-Term Memory (LSTM) along with DC battery degradation analysis which constitutes the optimization model. The results showed that the VPP EMS can perform optimal power scheduling with the day ahead and real-time electricity price scenarios. Moreover, further analysis including battery degradation cost of DC UPS ESS proved the capability of the proposed framework by up to 2064.4 $/h revenue for day-ahead price and 562 $/h revenue for the real-time price compared to revenue without battery degradation cost. A simulation scenario is also carried out where the constant load demand and the battery storage powers are varied between 50% and 150%. It can be concluded that the 500 kW and 750 kW ESS (100% and 150%) are better choices as compared to 250 kW (50%) during increased load demand for investors due to their higher revenue. Hence it is concluded that this research highlights the importance of revenue benefits from deploying VPP with the collaborative operation of DERs.

Optimal Location and Sizing of Distributed Generators and Energy Storage Systems in Microgrids: A Review

This article reviews the main methodologies employed for the optimal location, sizing, and operation of Distributed Generators (DGs) and Energy Storage Systems (ESSs) in electrical networks. For such purpose, we first analyzed the devices that comprise a microgrid (MG) in an environment with Distributed Energy Resources (DERs) and their modes of operation. Following that, we examined the planning and operation of each DER considered in this study (DGs and ESSs). Finally, we addressed the joint integration of DGs and ESSs into MGs. From this literature review, we were able to identify both the objective functions and constraints that are most commonly used to formulate the problem of the optimal integration and operation of DGs and ESSs in MGs. Moreover, this review allowed us to identify the methodologies that have been employed for such integration, as well as the current needs in the field. With this information, the purpose is to develop new mathematical formulations and approaches for the optimal integration and operation of DERs into MGs that provide financial and operational benefits.

Editorial Market-based distributed energy resources operation for future power systems

Editorial Market-based distributed energy resources operation for future power systems

Review of Methods for Addressing Challenging Issues in the Operation of Protection Devices in Microgrids with Voltages of up to 1 kV That Integrates Distributed Energy Resources

With the large-scale integration of distributed energy resources (DER) into passive distribution networks with voltages of up to 1 kV, these networks are being converted into microgrids. When the topology and operating conditions change, several challenging issues arise related to the functioning of the protection devices (PD) that are in operation. Most DERs, including renewable generators, are integrated into microgrids by means of inverters. In the event of short circuits (SC) in microgrids, these DERs provide a fault current contribution of no more than 1.2–2.0 Irated at the fault location. This makes it difficult to identify the fault location and to carry out the selective disconnection of the faulty element by means of conventional PDs. This article provides an overview of engineering solutions for improving conventional protection schemes that have been historically used in passive distribution networks, as well as for creating modern protection schemes based on innovative principles and new methods. The use of adaptive protections built on decentralized and centralized principles in most cases ensures the reliable protection of microgrids. Modern intelligent electronic devices (IEDs), where protection functions are implemented, rank higher with respect to their technical perfection in terms of reliability, sensitivity, selectivity, and speed performance. The use of multi-agent systems in the implementation of modern protection schemes requires the availability of broadband communication channels, which hinders their use because of the high cost. The combined use of fault current limiters (FCL) and energy storage systems (ESS) allows for the reliable operation of microgrid protections. The use of modern PDs ensures the reliable operation of DERs and power supply to consumers in microgrids, both in the case of grid-connected and islanded operation modes. Since there is no unified concept of designing protection schemes for microgrids with DERs, the choice of specific approaches to the design of protection schemes should be based on the results of a comparative technical and economic analysis of different options.

A Comprehensive Tool for Scenario Generation of Solar Irradiance Profiles

Despite their positive effects on the decarbonization of energy systems, renewable energy sources can dramatically influence the short-term scheduling of distributed energy resources (DER) in smart grids due to their intermittent and non-programmable nature. Renewables’ uncertainties need to be properly considered in order to avoid DER operation strategies that may deviate from the optimal ones. This paper presents a comprehensive tool for the scenario generation of solar irradiance profiles by using historical data for a specific location. The tool is particularly useful for creating scenarios in the context of the stochastic operation optimization of DER systems. Making use of the Roulette Wheel mechanism for generating an initial set of scenarios, the tool applies a reduction process based on the Fast-Forward method, which allows the preservation of the most representative ones while reducing the computational efforts in the next potential stochastic optimization phase. From the application of the proposed tool to a numerical case study, it emerged that plausible scenarios are generated for solar irradiance profiles to be used as input for DER stochastic optimization purposes. Moreover, the high flexibility of the proposed tool allows the estimation of the behavior of the stochastic operation optimization of DER in the presence of more fluctuating but plausible solar irradiance patterns. A sensitivity analysis has also been carried out to evaluate the impact of key parameters, such as the number of regions, a metric, and a specific parameter used for the outlier removal process on the generated solar irradiance profiles, by showing their influence on their smoothness and variability. The results of this analysis are found to be particularly suitable to guide users in the definition of scenarios with specific characteristics.