Distributed Energy Resources Output Research Articles

Integrated Active and Reactive Power Control Methods for Distributed Energy Resources in Distribution Systems for Enhancing Hosting Capacity

Recently, there has been a significant increase in the integration of distributed energy resources (DERs) such as small-scale photovoltaic systems and wind turbines in power distribution systems. When the aggregated outputs of DERs are combined, excessive reverse current may occur in distribution lines, leading to overvoltage issues and exceeding thermal limits of the distribution lines. To address these issues, it is necessary to limit the output of DERs to a certain level, which results in constraining the hosting capacity of DERs in the distribution system. In this paper, coordination control methodologies of DERs are developed and executed to mitigate the overvoltage and overcurrent induced by DERs, thereby increasing the hosting capacity for DERs of the distribution system. This paper proposes three coordinated approaches of active and reactive power control of DERs, namely Var Precedence, Watt Precedence, and Integrated Watt and Var Control. The Var and Watt Precedence prioritizes reactive power for voltage (Q–V) and active power for current (P–I) to address network congestion, thereby enhancing hosting capacity. Conversely, the Integrated Var and Watt Precedence propose a novel algorithm that combines four control indices (Q–V, P–V, Q–I, and P–I) to solve network problems while maximizing hosting capacity. The three proposed methods are based on the sensitivity analysis of voltage and current to the active and reactive power outputs at the DER installation locations on the distribution lines, aiming to minimize DER active power curtailment. Each sensitivity is derived from linearized power equations at the operating points of the distribution system. To minimize the computation burden of iterative computation, each proposed method decouples active and reactive power and proceeds with sequential control in its own unique way, iteratively determining the precise output control of distributed power sources to reduce linearization errors. The three proposed algorithms are verified via case studies, evaluating their performance compared to conventional approaches. The case studies exhibit superior control effectiveness of the proposed DER power control methods compared to conventional methods when issues such as overvoltage and overcurrent occur simultaneously in the distribution line so that the DER hosting capacity of the system can be improved.

A Distribution Static Synchronous Compensator Application to Mitigate Voltage Variation for Distribution Feeders

With the growing penetration of distributed energy resources (DER), the accompanying challenges have led utilities to limit the hosting capacities of DER installations on distribution feeders. A distribution static synchronous compensator (DSTATCOM) is a power electronic device to provide dynamic injections and absorption of reactive power into the distribution grid with more flexible and reliable voltage control and power quality improvement. A distributed energy resources management system (DERMS) is developed to provide more effective control of a DSTATCOM that can help substantially increase hosting capacity and mitigate overvoltage problems with the existing feeder. A Taiwan power company’s (Taipower) feeder is selected for computer simulation, and the DSTATCOM is employed in the test feeder to demonstrate the effectiveness of the DSTATCOM in improving the overvoltage problems. The voltage/reactive power (Volt/VAR) control of the DSTATCOM helps reduce overvoltage/voltage fluctuations as the DER output increases.

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.

Efficient Distributed Energy Resource Voltage Control Using Ensemble Deep Reinforcement Learning

To meet the challenges of low-carbon power generation, distributed energy resources (DERs) such as solar and wind power generators are now widely integrated into the power grid. Because of the autonomous nature of DERs, ensuring properly regulated output voltages of the individual sources to the grid distribution system poses a technical challenge to grid operators. Stochastic, model-free voltage regulation methods such as deep reinforcement learning (DRL) have proven effective in the regulation of DER output voltages; however, deriving an optimal voltage control policy using DRL over a large state space has a large computational time complexity. In this paper, we illustrate a computationally efficient method for deriving an optimal voltage control policy using a parallelized DRL ensemble. Additionally, we illustrate the resiliency of the control ensemble when random noise is introduced by a cyber-adversary.

An analytical optimum method for simultaneous integration of PV, wind turbine and BESS to maximize technical benefits

AbstractThe present paper introduces an analytical approach for integrating distributed energy resources (DERs) and battery energy storage systems (BESSs) into power grids. The given method aims to simultaneously identify optimal buses and capacities of DERs and BESSs, with regard to the responsive load demand along with the stochastic nature of wind turbine (WT) and photovoltaic (PV) units, and then to minimize energy losses and improve voltage profiles. For this purpose, the responsive load model, the uncertainties in the WT and PV units, and the BESSs are firstly modeled. Secondly, the output curve of BESSs is obtained so that demand and supply can be balanced effectively, so as to utilize the full‐potential output of DERs. Thirdly, a set of formulation is developed for simultaneous integration of the DERs and the BESSs. This formulation reflects on different levels of loads and DERs for each period as well as each segment with their corresponding probability density. Finally, the proposed method is tested on the standard IEEE 33‐bus network. These results verify that sizing and sitting of DERs and BESSs can significantly shape the planning results of DERs and increase their penetration in the power grids at the same time.

Multilevel Dynamic Master-Slave Control Strategy for Resilience Enhancement of Networked Microgrids

Conventional power management methods of networked microgrids (NMGs) are limited to the failure of pinned communication terminals and heavy communication burdens. This paper proposes a multilevel dynamic master-slave control strategy via two-level dynamic leaders to realize the resilience enhanced power management of NMGs. The first level dynamic leader with considerations of distributed energy resources (DERs) feature is selected to guide the output of DERs and achieve the power management within individual microgrid (MG). Subsequently, the secondary level leader considering each MG feature is selected among the bidirectional interlinking converters (BICs), whose signals would be shared with other BICs by communication to achieve power management among MGs. Moreover, the local weight selecting method (LWSM) is proposed to automatically select the two-level dynamic leaders according to the real-time system operation state. Compared with conventional methods, the communication among MGs is essentially realized through the dynamic DER leaders instead of pinned ones. Therefore, unreliability issues in the event of pinned terminal outage and converters’ communication failure can be fully addressed and the communication bus within each only needs to transmit one DER’s signals. The proposed strategy can be also extended to NMGs with various topologies and provide the “plug and play” capabilities of DERs or MGs. Finally, the effectiveness and feasibility of the proposed strategy are verified through the PSCAD/EMTDC platform.

Ambiguities and nonmonotonicities under prosumer power

Prosumers adopt distributed energy resources (DER) to cover part of their own consumption and to sell surplus energy. Although individual prosumers are too dispersed to exert operational market power, they may collectively hold a strategic advantage over conventional generation in selecting DER capacity via aggregators. We devise a bilevel model to examine DER capacity sizing by a collective prosumer as a Stackelberg leader in an electricity industry where conventional generation may exert market power in operations. At the upper level, the prosumer chooses DER capacity in anticipation of lower-level operations by conventional generation and DER output. We demonstrate that exertion of market power in operations by conventional generation and the marginal cost of conventional generation affect DER investment by the prosumer in a nonmonotonic manner. Intuitively, in an industry where conventional generation exerts market power in operations similar to a monopoly (MO), the prosumer invests in more DER capacity than under perfectly competitive operations (PC) to take advantage of a high market-clearing price. However, if the marginal cost of conventional generation is high enough, then this intuitive result is reversed as the prosumer adopts more DER capacity under PC than under MO. This is because the high marginal cost of conventional generation prevents the market-clearing price from decreasing, thereby allowing for higher prosumer revenues. Moreover, competition relieves the chokehold on consumption under MO, which further incentivises the prosumer to expand DER capacity to capture market share. We prove the existence of a critical threshold for the marginal cost of conventional generation that leads to this counterintuitive result. Finally, we propose a countervailing regulatory mechanism that yields welfare-enhancing DER investment even in deregulated electricity industries.

Adaptive Synthetic Inertia Control Framework for Distributed Energy Resources in Low-Inertia Microgrid

Bulk integration of Distributed Energy Resources (DERs) into the power grids reduces the system’s inherent inertial response.The reduced inertial response causes a high Rate-of-Change-of-Frequency (RoCoF) and poses formidable operational challenges for the grid frequency stability. Interconnections around the world comprehend the role and value of the Synthetic Inertia Control (SIC), which is considered as a subset of the Fast Frequency Response (FFR) and as one of the potential solutions to arrest high RoCoF in low-inertia power systems. This paper proposes an intelligent SIC model with an adaptive Fuzzy Logic Controller for a low-inertia microgrid. The proposed Fuzzy-SIC (FSIC) design optimizes the DER output to fulfill the FFR requirements of the system for various operating conditions. The particle swarm optimization algorithm is applied to tune the SIC unit parameters along with the secondary Proportional–Integral–Derivative control. The proposed approach is examined in a control area with distinct degrees of DERs and load. Case studies and numerical results demonstrate about 87% improvement in RoCoF and frequency nadir in comparison to the system without a synthetic inertia emulation. Furthermore, the robustness of the proposed approach is evaluated using various case studies and a time-domain analysis is conducted to demonstrate the impact of incremental SIC parameters on the system parameters.

An Incentive-Based Mechanism to Alleviate Active Power Congestion in a Multi-Agent Distribution System

Increasing the integration of distributed energy resources (DERs) inspired by environmental and governmental incentives, beside the introduction of multi-agent structure perspective have led to a paradigm shift of operational conditions in distribution systems. In this context, the traditional concept of fit and forget in the distribution grid management would not be efficient in the modern distribution systems and consequently new mechanisms should be developed in order to enable the distribution system operator (DSO) to efficiently manage the grid congestion caused by peak power output of DERs or load demands requests. In this article, the Stackelberg game concept is employed to develop an incentive-based mechanism that facilitates the contribution of local flexible resources operated by independent agents in the congestion management of distribution grids. Therefore, the proposed bi-level formulation would result in congestion alleviation in the multi-agent system; while the objectives associated with DSO and independent agents are fulfilled. Additionally, the developed framework enables DSO to reconfigure the network to decrease the operational costs associated with the congestion alleviation procedure. Finally, the strong-duality concept is utilized in order to combine the two-level problem into a one-level problem and the obtained model is implemented on IEEE-37 bus test system to investigate its effectiveness in congestion management in the distribution system.

Multi-timescale active distribution network optimal scheduling considering temporal-spatial reserve coordination

To improve the self-adjusting ability of the distribution network and reduce the impact of fluctuated power on the upstream transmission network, a multi-timescale active distribution network (ADN) scheduling method, which includes a reserve coordination strategy and a scheduling framework, is proposed in this paper. The reserve coordination strategy can schedule available reserve resources according to their temporal-spatial characteristics, and the scheduling framework further uses the reserve margin as feedback signals and optimizes resources in both the transmission and distribution network under different timescale. In the day-ahead stage, reserve provided by transmission network and operation of slow-response resources are optimized based on the day-ahead forecast. In the intraday stage, the reserve and output of remaining distributed energy resources (DERs) in the distribution network are updated according to the renewed forecast with more accuracy. In the real-time stage, combining with the model predictive control (MPC), deviations from the dispatched results are minimized to adjust the output of DERs on a rolling basis. The temporal reserve coordination for day-ahead and intraday can reduce the real-time deviation, and the spatial reserve coordination for transmission and distribution network can minimize the impact of power fluctuations on the upstream transmission. Case study on an IEEE 33-bus system verified the effectiveness of the proposed method.

Increment‐correction method for online distributed optimisation in distribution networks with extra‐high DER integration

This study focuses on future distribution networks featuring the integration of extra-high distributed energy resources (DERs). In these distribution networks, the extra-high DER penetration and the randomness and volatility of DER outputs increase the likelihood of voltage problems. Hence, it is necessary to design efficient solutions for DER output setpoints to regulate the voltages of distribution networks. However, the existing online distributed algorithms were designed based on different linearised technologies for power flow equations, which will cause imprecise results under extra-high DER integration. In this study, the authors propose an increment-correction method to improve the linearised power flow model. To obtain an efficient distributed manner, they introduced an indirect function into the end-customer (the DER owner)'s objective function, stimulating them to provide voltage-regulating service. In particular, two price incentive signals contained in the indirect function were transmitted from the distribution system operator to drive the DER owner to regulate output increments, which is suitable for cases where the DER belongs to different end-customers. The proposed method can not only protect the private information of end-customers but also enhance the robustness of the distribution network for the DER integration level. Numerical test results demonstrate the merits of the proposed method.

Design of a DSO‐TSO balancing market coordination scheme for decentralised energy

The proliferation of distributed generation and the electrification of heat and transport pose significant challenges to distribution system operators (DSOs) and transmission system operators (TSOs). These challenges include the choice between network upgrades or operating increasingly constrained networks, with a reliance on the flexibility of distributed energy resources (DERs). This study presents a novel market-based coordination scheme, which allows both the DSO and TSO to access DER flexibility, while respecting distribution system limits. The DSO's objective in this work is to minimise the cost incurred by DSO adjustments to DERs, required to ensure stable distribution network operation. The methodology presented has the advantages of being compatible with existing TSO balancing market operation, and scalable enough to include multiple DSO markets coordinating with the TSO. The approach is demonstrated on a section of Great Britain distribution network, using high DER growth scenario data for the year 2030. The case studies demonstrate the proposed DSO market mechanism to maintain thermal and voltage limits during periods of peak demand and DER output. The DSO is given priority in using DERs to solve distribution network constraints, however, significant flexibility remains for the TSO even during periods of peak demand and maximum export.

Tri-Level Robust Investment Planning of DERs in Distribution Networks With AC Constraints

Integration of distributed energy resources (DERs) has salient technical benefits such as improving power system resiliency, reducing transmission network losses, and regulating voltage in distribution networks. In addition to technical benefits, DERs bring remarkable economic profits for their investor. Accordingly, a comprehensive and accurate planning study is urgently needed by an investor who is to build DERs in a distribution network. In this paper, a tri-level investment planning model is proposed to maximize the net present value (NPV) of DERs in distribution networks. Robust optimization is utilized to cope with physical and financial data uncertainty in load demand, DERs output, and energy selling price. An effective decomposition strategy called column and constraint generation (C&CG) method is adapted to link the first and second levels of the proposed optimization problem. An iterative AC optimal power flow (AC-OPF) is presented to model the distribution grid circuit equations. The developed AC-OPF is solved in the third level through a modified Newton's method lied within a Benders decomposition framework. The proposed model is formulated as mixed-integer linear programming (MILP) problem and its effectiveness is illustrated by the IEEE 33-bus distribution test system.

Distributed Load Restoration in Unbalanced Active Distribution Systems

The resilience of modern power systems has been constantly threatened by various natural disasters and man-made attacks. Under those threats, fast and automatic restoration actions are critical for restoring impacted systems to normal operating conditions and preventing from additional disastrous consequences. Smart grid technologies, such as distributed energy resources (DERs) and microgrids, provide both opportunities and challenges for distribution system restoration. In this paper, distributed load restoration (DLR) in unbalanced active distribution networks is developed using the alternating direction method of multipliers (ADMMs) algorithm. The step-by-step restoration actions are provided to schedule the output of DERs and distribution system control devices, as well as the power consumption from transmission system restoration. The nonlinearities from three-phase unbalanced power flow and distribution components modeling are relaxed into a convex quadratic programming model. Then, the problem is decomposed into subproblems for each node by applying ADMM-DLR; and solves through each agent by exchanging limited information with neighboring nodes in an iterative procedure. The developed models and algorithms are validated and demonstrated through testing of the IEEE 13-node and 123-node test feeders. Simulation results demonstrate the effectiveness of restoring unbalanced distribution system with the hybrid bottom-up and top-down restoration strategies.

Network-Cognizant Voltage Droop Control for Distribution Grids

This paper examines distribution systems that have a high integration level of distributed energy resources (DERs), and addresses the design of local control methods for real-time voltage regulation. Particularly, the paper focuses on proportional control strategies wherein the active and reactive power output of DERs are adjusted in response to (and proportionally to) local changes in voltage levels. The design of the voltage-active power and voltage-reactive power characteristics leverages suitable linear approximations of the ac power flow equations and is network-cognizant; that is, the coefficients of the controllers embed information on the location of the DERs and forecasted noncontrollable loads/injections and, consequently, on the effect of DERs power adjustments on the overall voltage profile. A robust approach is pursued to cope with uncertainty in the forecasted noncontrollable loads/power injections. The stability of the proposed local controllers is analytically assessed and numerically corroborated.

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.

Modeling study on flexible load's demand response potentials for providing ancillary services at the substation level

Demand response (DR) is an important component for the establishment of smart electricity grids. It can decrease the system peaks through load shedding or shifting and optimize the utilization of the existing grid assets, which delays the need for costly upgrades. DR can also enable the integration of intermittent and distributed energy resources (DER) into the existing electricity grid. Fast DR from aggregated flexible loads can provide ancillary services (AS) to absorb grid disruptions and may replace the expensive fast-ramping reserve generation units. This study presents a methodology for load aggregation based on the prioritization of loads according to their flexibility. Different flexible load types are categorized as thermostatically controlled loads (TCL), urgent non-TCL, non-urgent non-TCL, and battery-based loads. Models based on their physical behaviour are developed and simulations performed to apply the proposed aggregation and control algorithm. Results show that the loads during peak hours can be shed off without rebound demand spikes after the DR event commonly seen in other types of DR programs. The algorithm also automatically adjusts the power demand according to the output of the distributed renewable generation, mitigating disruptions due to variations of the DER output. Additionally, the algorithm is able to adjust the load demand dynamically according to the fluctuations of electricity price.

Fair cost distribution among smart homes with microgrid

Microgrid is composed of a set of distributed energy resources (DER) and is considered as an alternative energy providing system to the current centralised energy generation. Smart homes equipped with smart grid technology, such as smart meter and communication system, are becoming popular for their lower energy cost and provision of comfort. Flexible energy-consuming household tasks can be scheduled coordinately among multiple homes which share the common microgrid. When local DERs cannot fulfill the whole demand, smart homes will compete with each other to obtain energy from local DERs and achieve their respective lowest energy cost. In this paper, a mathematical programming formulation is presented for the fair cost distribution among smart homes with microgrid. The proposed model is based on the lexicographic minimax method using a mixed integer linear programming (MILP) approach. One-day forecasted energy cost of each smart home is minimised under fairness concern. DER operation, DER output sharing among smart homes and electricity consumption household tasks are scheduled. Two numerical examples with 10 and 50 smart homes are studied. The computational results illustrate that the proposed approach can obtain obvious cost savings (30% and 24% respectively) and fair cost distribution among multiple homes under given fairness scenario.

Assessing the Impact of SiC MOSFETs on Converter Interfaces for Distributed Energy Resources

Distributed energy resources (DERs) are becoming integral components of electric power distribution systems. In most cases, an isolated DC-DC converter forms part of the interface required to connect the DER output to the distribution system. Operation of the converter at high switching frequencies results in size reduction of passive components at the expense of increased switching losses. However, silicon carbide (SiC) power devices have the potential of operating at high switching frequencies without significant loss penalty because of their fast switching times and ability to work at high temperatures when compared to similar Si devices. SiC diodes have already displayed the ability to offer more ideal diode behavior than Si diodes. Engineering samples of SiC MOSFETs are depicting lower switching losses and conduction losses over Si MOSFETs. This display is making SiC devices attractive for DC-DC converters used to connect DERs to the distribution system. This paper particularly deals with the design of a 300 W 100 kHz DC-DC full-bridge converter using zero-voltage zero-current switching for comparison of SiC MOSFETs and diodes against Si MOSFETs and diodes.