Dispatch Of Distributed Energy Resources Research Articles

Dynamic security constrained AC optimal power flow for microgrids

To ensure the continuous operation of a microgrid, proactive planning is essential, especially when contemplating possible dynamic events that alter the scheduled scenarios for the islanded operation or during its transition from connected to islanded mode. This paper introduces an innovative mathematical programming model for the AC optimal power flow (AC-OPF) with dynamic security constraints (DSCs), considering two scenarios: the islanded operation and the transition. This model uses positive-sequence equations to represent the inverter-based resources (IBRs) for grid-forming and grid-following roles, along with a fourth-order synchronous generator model equipped with excitation and frequency control systems. They assess the dynamic response to specific events such as short-circuits, decreases in PV generation, increases in load demand during the islanded operation, and the transition itself. The DSCs are applied to dispatchable distributed energy resources (DERs), which react to variations in the microgrid, considering generation and opportunity costs to minimize the discrepancy between planned and secure operating points. The mathematical programming model is implemented using AMPL, and solutions are obtained through the nonlinear optimization solver IPOPT. The tests are conducted in an adapted version of the microgrid being developed at the University of Campinas that includes a synchronous generator, photovoltaic (PV) generation, and a battery energy storage system (BESS). Results demonstrate the model’s effectiveness in adjusting generation dispatch to withstand defined events and optimizing generation resources, even when limits are not reached.

Analysis of microgrid configuration with optimal power injection from grid using point estimate method embedded fuzzy-particle swarm optimization

This paper aims to analyze the effect of integration of combined heat and power (CHP) units, renewables, shunt capacitors, and a heat boiler in a grid connected microgrid (GMG) system. In addition to that, a comparative study is conducted between the GMG and zero bus concept based microgrid (ZBMG) systems. In ZBMG, a predefined amount of power is injected from the grid. Therefore, one of the dispatchable distributed energy resources (DERs) in the ZBMG maintains the power balance and the corresponding DER unit is entitled as zero bus DER. In this paper, eight different microgrid configurations are formed by several combinations of renewable and non renewable sources, which are categorized under GMG and ZBMG systems. The multiple objectives such as total cost, emission, and energy loss are combined and solved using fuzzy technique and particle swarm optimization (PSO) algorithm. Also, point estimate method (PEM) is implemented to handle the uncertainty in renewables. The effect of grid, dispatchable units, renewables, and heat boiler on the objectives in each microgrid configuration is inspected. The results show that the microgrid configuration with non renewables is attained minimum cost and has a minimum emission with renewables. The integration of all types of sources in a microgrid configuration leads to a decrement in all the objectives. The proposed method is tested on a 33-bus grid connected microgrid system considering electrical and heat loads.

Behavior of prosumers in Smart Grid: A comparison of Net Energy Metering and Billing Schemes, and Game Theory-based Local Electricity Market

Net energy metering (NEM) and net energy billing (NEB) are the most commonly used self-consumption schemes for prosumers in countries around the world. The concept of the local electricity market (LEM) is emerging as the potential successor of the self-consumption schemes, with several already implemented pilot projects. The comparative study of prosumers’ behavior in terms of optimal economic self-dispatching under NEM, NEB, and game-theory-based LEM energy policies is performed in this paper. The non-cooperative game-theoretic methodology is used for the calculation of Nash–Cournot equilibrium which is adopted as an LEM clearing mechanism. The prosumers’ daily energy patterns (generation, consumption, and storage) and payoffs are analyzed. Diversities of prosumers’ assets (distributed energy resources (DERs) and storage devices) and load profiles are considered. The distribution utility’s role is considered in terms of its own payoff for supplying the necessary energy to ensure the power balance equation. The numerical analysis is performed using the model of the benchmark test distribution network IEEE 33-node. The obtained results are discussed in terms of the correlation between prosumers’ behavior and the adopted energy policy. The strengths and weaknesses of the most common energy policies are highlighted from the prosumers’ perspective. The obtained results confirm that the transition from NEM to NEB self-consumption scheme could provide a better payoff to prosumers with dispatchable DERs under variable electricity price for excess energy produced. The residential prosumers equipped with SDs or dispatchable DERs were able to achieve the best payoffs in the case of Game Theory-based LEM.

Transactive Control of Electric Railways Using Dynamic Market Mechanisms

Electricity demand of electric railways is a relatively unexplored source of flexibility in demand response applications in power systems. In this article, we propose a transactive control-based optimization framework for coordinating the power grid network and the train network. This is accomplished by coordinating dispatchable distributed energy resources (DERs) and demand profiles of trains using a two-step optimization. A railway-based dynamic market mechanism (rDMM) is proposed for the dispatch of DERs in the power network along the electric railway using an iterative negotiation process, generates the profiles of electricity prices, and constitutes the first step. The train dispatch attempts to minimize the operational costs of trains that ply along the railway, while subject to constraints on their acceleration profiles, route schedules, and the train dynamics, and generate demand profiles of trains and constitute the second step. The rDMM seeks to optimize the operational costs of the underlying DERs while ensuring power balance. Together, they form an overall framework that yields the desired transactions between the railway and power grid infrastructures. This overall optimization approach is validated using simulation studies of the Southbound Amtrak service along the Northeast Corridor (NEC) in USA, which shows a 25% reduction in energy costs when compared to standard trip optimization based on minimum work and a 75% reduction in energy costs when compared to the train cost calculated using a field dataset.

Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization.

In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.

Deep Reinforcement Learning From Demonstrations to Assist Service Restoration in Islanded Microgrids

Microgrids can be operated in island mode during utility grid outages to support service restoration and improve system resilience. To schedule and dispatch distributed energy resources (DERs) in an islanded microgrid, conventional model-based methods rely on accurate distribution network models and lack generalization and adaptability. Data-driven methods are promising for DER coordination but face practical challenges such as potential hazards to microgrids during online training and insufficient online training opportunities due to low outage rates. This paper presents a novel two-stage learning framework to identify an optimal restoration strategy. The proposed framework builds on the deep deterministic policy gradient from demonstrations, which is a dataset that contains a trajectory of states and the associated expert actions. At the pre-training stage, imitation learning is applied to equip the control agent with expert experiences to guarantee acceptable initial performance. At the online training stage, action clipping, reward shaping, and expert demonstrations are leveraged to ensure safe exploration while accelerating the training process. The proposed method is illustrated using the IEEE 123-node system and compared with a representative model-based method and the standard deep deterministic policy gradient method to prove solution accuracy and demonstrate increased computational efficiency.

Auctions for Nonwires Alternatives: Securing and Operating Dispatchable Distributed Energy Resources

As Distributed energy resources (DERs) decline in cost, improve in capability, and receive more interest from end consumers, there are increasing opportunities for them to play a role in managing the distribution system. One of the major ways DERs can provide services to the electricity system is by acting as nonwires alternatives (NWAs) to manage local peak demand and help defer or avoid capital and operational costs associated with traditional network infrastructure. NWA projects can help utilities and regulators reduce system costs and costumer bills if DERs are the less costly solution for meeting incremental load growth.

Modeling Environment for Testing a Distributed Energy Resource Management System (DERMS) Using GridAPPS-D Platform

The electric power system is currently undergoing a major transition due to growing numbers of distributed energy resources (DERs) and increased distribution automation. If optimally managed and operated, DERs could provide flexibility and highly valuable grid services such as restoration, peak shaving, voltage regulation, and frequency support to maintain grid reliability. Different applications and enterprises, such as distributed energy resources management systems (DERMS), are being developed for coordinated and optimal operation of DERs. However, to attract sufficient DER participation and achieve the coordinated operation of DERs, systems and components must be interoperable and information exchange must be secure. Along this line, the Portland State University power engineering group is developing Energy Grid of Things (EGoT) DERMS prototype. The proposed application requires the coordinated dispatch of large numbers of DERs and testing such a system presents a challenge; it is not practical to test system prototypes using thousands of real DERs. Hence, the modeling environment (ME) is designed as a co-simulation tool to model interactions between a DERMS and a mass of simulated DERs. The ME is expected to address the scalability issue inherent to hardware-in-the-loop DERMS simulation; many assets are needed to observe effects on the grid from deployment and dispatch of DERs. To enable the development and testing of such advanced applications for power distribution system planning and operations, the U.S Department of Energy developed GridAPPS-D™, an open-source, standards-based platform at Pacific Northwest National Laboratory. This paper introduces the proposed ME for testing a DERMS application. The architecture of the ME is presented, and the GridAPPS-D features for a such simulation environment are discussed. Additionally, the procedure for developing the ME within the GridAPPS-D platform and use of different APIs for efficient and timely integration are discussed in detail.

Real-time optimization of active distribution networks with distributed energy resources participating in frequency regulation

Dispatchable distributed energy resources (DERs) in distribution networks are envisioned to aid frequency regulation for transmission systems. In this paper, a real-time optimal dispatch framework for DERs in distribution networks is designed to offer frequency regulation services simultaneously. Different from the existing research that distribution networks track uniaxially the predetermined auxiliary-services commands of the transmission system, here we regard transmission system frequency regulation as a black box and learn the parameters of its proxy satisfaction function from the perspective of DER optimization. To solve such a special optimization problem with control performance feedback, first we employ Gaussian processes to learn the satisfaction function, and, especially, build pertinent upper confidence bounds to achieve the optimal provision of ancillary services. Next, the primal-dual gradient projection process is embedded into the Gaussian process upper confidence bound algorithm to pursue the optimal DER dispatch. Accordingly, the output powers of DERs can be controlled in real-time: in disaggregate mode, they meet the goal of the distribution network; in aggregate mode, they provide a more satisfactory tie-line power flow to the transmission system. Simulations for illustrative systems are provided to validate the approach.

Two-Cored Energy Management System for Industrial Microgrid

Energy systems have to deal with energy cost and environmental concerns such as greenhouse gas emission. Industrial buildings considered as Microgrid (μG) with heavy load worsen these issues even more. Further, cyber-attacks on the data communication channel between utility and customer is also a potential threat and may alter the data as well as the confidentiality of it, resulting in an inaccurate result. To address these problems, this paper proposes two-cored Building Energy Management System (BEMS) for Industrial Microgrid (IμG) with first cored termed as the energy layer concentrating on energy cost and emission reduction, while second cored termed as security layer provides the un-authorized intrusion detection and prevention system (IDS/IPS) for cyber secure communication of data. The μG under consideration contains national grid, Natural Gas (NG), solar Photovoltaic (PV) as input carriers; electrical energy at output ports; electric vehicle (EV) fleet; battery bank; solar PV panel as non-dispatchable Distributed Energy Resources (DERs) and Internal Combustion Engine (ICE), Fuel Cell (FC) and Micro Turbine (MT) as dispatchable DERs. Energy layer optimization problem has been solved in MATLAB using flower pollination algorithm for µG energy consumption cost and emission reduction. To develop and analyze the security layer, Linux operating system based Smooth-sec software has been used. Devised security layer continuously monitors the network traffic between customers and BEMS as well as BEMS and utility server. During monitoring it distinguish the licensed user or malicious attacker to detect and prevent possible internal and/or external intrusions in the communication channel. Results show that EMS reduces energy cost and emission in addition to cyber security from internal threats. Proposed two-cored control may be manufactured for utilities to realize its benefits for industrial customers in a smart energy distribution system. Keywords: Demand Response, Energy Management System, Flower

Post-extreme-event restoration using linear topological constraints and DER scheduling to enhance distribution system resilience

In this paper, a post-extreme-event restoration (PEER) algorithm is proposed to improve distribution system resilience. Linear topological constraints are proposed to ensure radial topology after N-k contingencies, possibly in multiple islands. The approach is made comprehensive by considering dispatchable distributed energy resources (DERs), non-dispatchable DERs, and demand responses, as well as on-load tap changers (OLTCs) and shunt capacitors. The goal is to minimize the accumulative expense caused by load reduction payment or penalty, as well as DER operation cost. As a result, the overall system will survive longer with higher resilience during an extreme event. To verify the effectiveness of the PEER algorithm, a resilience evaluation algorithm is proposed using Monte Carlo simulation (MCS) with reduced scenarios. This is based on a probabilistic model for generating random scenarios which consider the uncertainty of line faults and solar irradiance. Combined with the proposed PEER algorithm, this reduced-scenario MCS can evaluate the expected energy not served (EENS) which is an essential index for distribution system resilience. Case studies of the IEEE 33-bus and 123-bus test systems validate the proposed algorithm in reducing EENS.

Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System

A microgrid energy management system (MEMS) optimally schedules the operation of dispatchable distributed energy resources to minimize the operation costs of microgrids (MGs) via an economic dispatch (ED). Actual ED implementation in the MEMS relies on an optimization software package called an optimization solver. This paper presents a comparative study of optimization solvers to investigate their suitability for ED implementation in the MEMS. Four optimization solvers, including commercial as well as open-source-based ones, were compared in terms of their computational capability and optimization results for ED. Two-stage scheduling was applied for the ED strategy, whereby a mixed-integer programming problem was solved to yield the optimal operation schedule of battery-based energy storage systems. In the first stage, the optimal schedule is identified one day before the operating day; in the second stage, the optimal schedule is updated every 5 min during actual operation to compensate for operational uncertainties. A modularized programming strategy was also introduced to allow for a comparison between the optimization solvers and efficient writing of codes. Comparative simulation case studies were conducted on three test-bed MGs to evaluate the optimization results and computation times of the compared optimization solvers.

Sliding-Mode Control of AC Voltages and Currents of Dispatchable Distributed Energy Resources in Master-Slave-Organized Inverter-Based Microgrids

This paper proposes a current-controlled voltage-mode control scheme for dispatchable electronically coupled distributed energy resource (DER) units based on sliding-mode control (SMC) strategy. The proposed control strategy provides fast and stable control on the terminal voltage and frequency of DER unit and ensures protection of the power-electronic interface to external faults. In addition it maintains the quality of the output voltage of the host DER unit in spite of unbalanced and/or distorted load currents. Performance of the proposed SMC strategy is demonstrated through time-domain simulation of a single islanded DER unit, starting from black-start and subjected to various operating scenarios, and it is compared to the performance of a proportional-integral (PI) based control strategy. Also a current-mode control strategy is proposed for DER units based on SMC. The performance of both voltage- and current-mode control strategies are demonstrated in a sample master-slave organized three-unit microgrid.

Application of the Conservative Power Theory Current Decomposition in a Load Power-Sharing Strategy Among Distributed Energy Resources

The conservative power theory (CPT) is a time-domain theory applicable to any periodic signal single- or polyphase system with or without a neutral conductor. The principle operation is the orthogonal decomposition of electrical variables, resulting in electrical quantities with physical meaning. An important feature of the CPT is the current decomposition. It consists of decomposing any current into five parts. The label of these parts is active current, reactive current, scattered active current, scattered reactive current, and generated current. This paper proposes the application of the current decomposition of the CPT in distributed energy resources (DERs). A supervisory controller (SC) uses the current decomposition in a load power-sharing strategy. The combination of these two issues is the main contribution of this paper. Moreover, the proposal of this paper is more attractive than the conventional droop strategy since the DERs follow their current reference in a current-controlled mode, making the load sharing faster, more reliable, and more accurate. A clear understanding of how the CPT decomposes load currents into its parts is presented, as well as the drawback found in droop strategy when dealing with load power sharing. Later, the SC load power-sharing strategy is described, indicating that the proposal of this paper makes an SC to have a broad option when managing a smart grid with dispatchable DERs. A case study with experimental results shows the efficacy of applying current decomposition of the CPT in load power-sharing strategies.

Control Strategy for Dispatchable Distributed Energy Resources in Islanded Microgrids

This paper proposes a multi-input muti-output control strategy for dispatchable power electronic based distributed energy resources (DERs) in islanded mode operation of microgrids. The controller is designed based on the dq model of the DER unit. Cascade, feedforward, and internal model control principle, incorporating the theory of integral control and repetitive controller, are used. The proposed controller regulates the voltage at the output of DERs under any load (balanced, unbalanced, and harmonic) variation and/or fault condition. The stability of the closed-loop system has been investigated. Enhanced phase-locked loop (EPLL) is used to determine the system variables in the dq frame under unbalanced (and balanced) operating conditions. The structure of the EPLL is further modified to regulate the microgrid frequency and a stability analysis of proposed frequency control loop is presented. The performance of the proposed controller is verified through test case studies, carried out on an autonomous microgrid with single and multiple DER units.

Optimal droop gains assignment for real‐time energy management in an islanding microgrid: a two‐layer techno‐economic approach

Real-time energy management is an important challenge in today's microgrids. In this study, a two-layer techno-economic energy management framework is proposed for an islanding microgrid. The minimisation of operation and emission costs are considered in the first layer procedure. Dynamic performance of dispatchable distributed energy resources is taken into account to improve the load following performance. Optimal operation of the microgrid is considered as an optimisation problem which is solved using modified particle swarm optimisation algorithm. In the second layer, optimal droop gains of micro-resources are assigned based on the combination of gradient descent method and minimum mean square frequency error algorithm. The proposed methodology is applied to a typical microgrid and its performance is evaluated. The simulation results show that using the proposed methodology, the frequency deviation of microgrid is reduced properly so that the results are better than the results of conventional methodology. Moreover, the microgrid energy management and power sharing between micro-sources are more economical in comparison with conventional methods.

Provisional internal and external power exchange to support remote sustainable microgrids in the course of power deficiency

This paper proposes a power exchange strategy for sustainable microgrids (MGs) of remote areas that have no access to a utility grid. This strategy manages the MG during power deficiencies under a decentralised approach (DA), which is the most probable case for remote MGs due to the lack of communication systems, or a centralised approach if a data communication system is available. Under each approach, a two-level (internal/external) control is established. The internal support is provided through power exchange with local energy storage while the external support is provided through power exchange with a neighbouring MG, after their temporary coupling. Appropriate conditions and constraints, under which the necessity and possibility of internal/external power exchange can be determined, are defined and formulated. These terms are based on local frequency measurements (for DA) and instantaneous power generation of the dispatchable distributed energy resources (for centralised approach). The dynamic performance of a remote system, composed of two MGs, operating with this strategy is evaluated by simulation studies in PSCAD/EMTDC. Furthermore, the small signal stability of such a system is investigated in MATLAB.

A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet

With a higher penetration of distributed generation in the power system, the application of microgrids is expected to increase dramatically in the future. This paper proposes a novel method to design optimal droop coefficients of dispatchable distributed energy resources for a microgrid in the Energy Internet considering the volatility of renewable energy generation, such as wind and photovoltaics. The uncertainties of renewable energy generation are modeled by a limited number of scenarios with high probabilities. In order to achieve stable and economical operation of a microgrid that is also suitable for plug-and-play distributed renewable energy and distributed energy storage devices, a multi-objective optimization model of droop coefficients compromising between operational cost and the integral of time-weighted absolute error criterion is developed. The optimization is solved by using a differential evolution algorithm. Case studies demonstrate that the economy and transient behavior of microgrids in the Energy Internet can both be improved significantly using the proposed method.

Assessing the financial impacts of distributed energy on load serving entities

This article analyzes the financial impact of distributed energy resources (DERs) owned and operated by commercial customers on the load serving entities (LSEs). DERs reduce the customers' electricity bills and hence the revenues collected by their LSE. However, changes in customer demand profiles can potentially reduce the aggregated system demand profile, and therefore, reduce the LSE's costs in wholesale markets. Analysis of these financial impacts indicates that the LSE's lost revenue ultimately outweighs its reduced expenses. This is largely due to a significant reduction in revenue from demand charges. Dispatchable DERs, including energy storages and demand response, result in more financial losses for LSEs than photovoltaics. The financial losses LSEs face indicate that redesigning commercial customer tariffs is necessary in order for LSEs to accommodate customer owned DERs properly. Several suggestions on modifying commercial tariffs are presented.

Development of Economic and Stable Power-Sharing Scheme in an Autonomous Microgrid with Volatile Wind Power Generation

—This article investigates the problem of economic and stable power sharing in an autonomous microgrid including a variety of dispatchable distributed energy resources and wind turbine generator. To minimize the operational cost of the microgrid considering the nature volatile of wind power generation, the frequency-droop coefficient of each dispatchable distributed energy resource dispatchable distributed energy resource should be properly scheduled to yield the desired power sharing among multiple dispatchable distributed energy resource dispatchable distributed energy resources. Also, it has previously been shown that the selection of frequency-droop coefficients has a significant effect on the small signal stability of the system. Consequently, a microgrid may suffer economic or stability degradation as variations in wind power generation and load demand if inappropriate frequency-droop coefficients are employed. This article therefore presents a detailed procedure of obtaining optimal frequency-droop coefficients of dispatchable distributed energy resources. The optimal droop coefficients of dispatchable distributed energy resources are obtained according to an optimization problem. The eigenvalue-based stability constraints are imposed on the objective function, in particular, to ensure that the microgrid system has a sufficient stability margin at all possible operation conditions. Particle swarm optimization is used to solve this problem. The simulation results show that the economy and stability of the microgrid can be improved significantly using the proposed method.