Distributed Generation Output Research Articles

A Microgrid Operation Control Strategy Based on Wide Area Information

Distributed Generator (DG) has many environmental benefits and potential values, but it is not controllable power for the large power grid, and brings many bad influences to the power grid, so that the power grid takes the way of isolation and restrictions that greatly limits the development of DG. In order to effectively solve the contradictions between DG and the power grid, the microgrid concept is putted forward. Microgrid is a small power system containing micro powers and local loads, which can operate with connecting to the power grid, or run in isolated island way with breaking away from the power grid. On the basis of analyzing the principle of DG, two control methods are established: PQ Control and V/f Control. And then, a microgrid operation control strategy based on wide area information is proposed. Using wide area information to get the following data: the switching power and the breaker state of the Point of Common Coupling (PCC); the output of DG; the local loads and so on. In the operation of isolated island, the control strategy controls and adjusts the DGs and loads quickly, makes the power balance, ensures that the voltage and frequency in the microgrid conforms to the standard, and ensures the important loads working steadily and reliably. Finally, the feasibility and effectiveness of the proposed control strategy are verified by matlab/simulink simulation.

Energy Management of Grid Interconnected Multi-Microgrids Based on P2P Energy Exchange: A Data Driven Approach

Grid interconnected multi-microgrids provides potential benefits to the consumers, where the microgrids (MGs) equipped with distributed generators (DGs), energy storage systems (ESSs), and diesel generators. However, intermittency of DGs, high cost of ESSs, and depleting fossil fuels are the major challenges for the economic operation of interconnected multi-microgrids. One potential way to address these challenges is to develop an energy management strategy (EMS) for the grid interconnected multi-microgrids. This paper proposes an EMS to reduce consumer energy consumption cost (ECC) using fuzzy-based peer-to-peer (P2P) energy exchange algorithm with dynamic pricing. In this context, the MGs consumers load power demand (LPD) and DGs output behaviors are modeled using random vector functional link network approach to predict future time slot values. Then, a fuzzy-based P2P energy exchange algorithm is developed to enable the surplus energy transfer to grid and/or MGs with dynamic pricing. Furthermore, an ESS charging/discharging energy control and diesel generator turn on strategies are developed based on the MGs deficit power. Then, the MGs consumer LPD reduction strategy is implemented based on the consumer ECC margin and energy consumption index. Finally, an EMS is proposed that includes on demand-supply strategy and consumer energy consumption cost reduction strategy based on the future time slot values. The novelty of the proposed work lies within the energy management of grid interconnected multi-microgrids and the reduction of consumers ECC through surplus energy transfer to grid and/or MGs using fuzzy-based P2P energy exchange algorithm with dynamic pricing. Historical data are used to demonstrate the effectiveness of the proposed EMS for grid interconnected multi-microgrids.

Coordinated virtual resistance and capacitance control scheme for accurate reactive power sharing and selective harmonic compensation in islanded microgrid

Line impedance mismatch and unregulated harmonic currents cause serious problems for an islanded microgrid, such as inaccurate reactive power sharing and voltage distortion at the point of common coupling (PCC). To overcome these issues, a coordinated virtual resistance and capacitance are introduced together with its control scheme for parallel distributed generation (DG) in an islanded microgrid. The proposed coordinated virtual impedance control scheme directly modifies the DG output impedance at the fundamental frequency to realise accurate current sharing among DG units. In addition, a variable harmonic impedance control loop is proposed for each DG to absorb the non-linear load harmonic current. Thanks to the proposed control scheme, the PCC harmonic voltage distortion is successfully compensated and power-sharing among DG units is accurately achieved. The stability of the microgrid system was analysed in detail to verify the feasibility of the coordinated virtual impedance control scheme. Experiments on a laboratory prototype microgrid are performed to validate the performance of the proposed control scheme.

Applying multiple types of demand response to optimal day‐ahead stochastic scheduling in the distribution network

With the increasing power demand of end-users, demand side management has been an important resource to been applied to the optimal day-ahead scheduling of distribution network. This work deeply studies the mechanism of multi-type demand response projects participating in load management. The authors particularly design the novel compensation mechanisms of interruptible load (IL) and transferable load (TL). The step-wise price-quantity offer package including basic response stage and elastic stages is devised for IL while the two-dimensional alternating function based on transferable time and transferable capacity is set up to quantify the load shifting cost for TL. To model uncertainties of distributed generation output power and response behaviour of end-users, a large number of stochastic scenarios are generated by the Monte Carlo method and Latin Hypercube Sampling and clustered into some typical scenarios by K-means method. Then, the optimal day-ahead stochastic scheduling model of the distribution network is proposed based on the obtained typical scenarios. In addition, three indexes including the contribution degree, confidence degree and strategy confidence degree are put forward to evaluate the rationality of the scheduling decision. Finally, the numerical results on the IEEE 33-bus network verify the effectiveness of the proposed model.

Distributed cooperative control strategy for islanded microgrids

In this paper, a distributed cooperative control strategy for islanded microgrids (MGs) is proposed, to minimize the total generation cost of distributed generators (DGs), and maintain the state of charge (SoC) balancing of battery energy storage systems (BESSs). First, a consensus-based economic dispatch (ED) strategy is proposed, to achieve the minimum total generation cost of DGs, while the aggregated power output of DGs meets the power dispatch command from the supervisory control system. Further, an SoC-based droop control strategy is developed for BESSs, to maintain the SoC balancing and power balance in the islanded MG simultaneously. Compared with the centralized control schemes, the proposed cooperative control strategy is fully distributed such that it simply requires local information exchange without the necessity of a central controller. More importantly, the proposed cooperative control strategy is able to achieve multiple control objectives in a unified framework. Finally, the simulation results in an islanded MG with renewable energy and time-varying load demands are presented, to demonstrate the effectiveness of the proposed distributed cooperative control strategy.

Dynamic reconfiguration of unbalanced distribution network considering DG uncertainties

In order to solve the multi-objective dynamic reconfiguration problem of the unbalanced source distribution network, this paper proposes a multi-objective dynamic reconfiguration strategy for active distribution network based on affine number. The affine number is introduced in this paper to describe the uncertainty of distributed generation output. The active network loss, voltage deviation and load balance are the objective functions, the dynamic reconfiguration of unbalanced distribution network is modeled. The multi-objective particle swarm optimization algorithm based on Pareto entropy and parallel mesh is used to obtain the static reconfiguration solution in hours. Finally, a reconfiguration time division scheme based on Mahalanobis distance and target expectation is proposed, and the time period is divided again until the switch action constraint is satisfied.

Unified data‐driven stochastic and robust service restoration method using non‐parametric estimation in distribution networks with soft open points

The service restoration is a vital measurement to enhance the resilience of the electrical distribution network. With the integration of distributed generations (DGs) in the distribution network, the uncertainties are introduced, which have a considerable impact on service restoration. Meanwhile, the soft open points (SOPs) are electronic devices installed in the tie-line of the distribution networks. They have the ability to control the branch power flow, which can be utilised to assist the service restoration. This study proposes a data-driven stochastic and robust service restoration method to handle the uncertainties of DGs outputs, which is modelled as a two-stage conic optimisation programming problem. The non-parametric estimation method is introduced to estimate the probability density function of forecasted error based on historical data. Furthermore, the nonlinear constraints of the SOPs are reformulated into conic forms so that the proposed model could be solved efficiently. Numerical tests are carried out on the IEEE 33-bus and 123-bus distribution networks to show the effectiveness of the proposed method, and the results show the superiority of the proposed method.

Fault Reconfiguration of Distribution Networks with Soft Open Points Considering Uncertainties of Photovoltaic Outputs and Loads

Conventional fault reconfiguration methods assume determinant values for loads and distributed generators outputs, which may lead to a reconfiguration solution that cannot satisfy operational constraints. This paper proposes a fault reconfiguration method for distribution networks with soft open points (SOPs) considering the uncertainties of photovoltaic (PV) output power and loads. Firstly, a fast probabilistic power flow method based on voltage sensitivity for distribution networks with SOPs is proposed to evaluate each reconfiguration scheme. Then a fault reconfiguration model considering uncertainties of PV output powers and loads is formulated and solved to minimize load shedding and line losses. The effectiveness of the proposed reconfiguration method is verified using the modified 33 and 69-bus distribution systems. The proposed method is shown to be very fast and can provide reliable reconfiguration schemes satisfying all system operation constraints.

Reliability Evaluation of Distribution System Considering Distributed Generation Correlation

The correlation between distributed generation (DG) can affect the reliability of distribution networks. To analyze the influence of this factor, first use Latin Hypercube Sampling, combine Spearman rank correlation coefficient and Cholesky decomposition to obtain the DG output power sample of the specified correlation coefficient. The bidirectional hierarchical structure reliability evaluation algorithm considering switch failure is used to evaluate the system. Considering the island operation mode of the distribution network after DG access, an improved heuristic load reduction strategy is proposed, and the reliability index of the load in the island is revised. The experimental results show that the reliability index of the system is closely related to the rated capacity of DG, the correlation between DG, and different load reduction strategies.

Optimal energy management of microgrid based on multi-parameter dynamic programming

With the wide application of microgrid system, fluctuation and randomness are the characteristics of distributed generation output. The traditional energy management system can’t meet the requirements to ensure the security and stability of the grid. The microgrid energy management is of great significance to the stable operation of power grid. In order to obtain higher economic benefits and pay less environmental costs, reasonable scheduling of various distributed power sources is able to achieve this goal. In this article, microgrid energy management including distributed generation is studied. The objective function includes the economic objective and the environmental objective. The model of energy management is considered as a multi-objectives and multi-parametric optimization problem. The multi-parameter dynamic programming is used to optimize the energy management of microgrid. Finally, the efficiency of the proposed method is examined by the simulation studies.

Probabilistic generation model for optimal allocation of wind DG in distribution systems with time varying load models

Renewable energy-based Distributed Generation (DG) is the most imperative part of modern power system and offers many potential benefits. To attain maximum benefits offered by DG integration, it is important to model the time varying characteristics of both load and generation. Therefore, this paper presents a new Weibull distribution-based time-coupled Probabilistic Generation model for optimal placement and sizing of wind DG with time varying voltage dependent (TVVD) loads. At first, Probabilistic model is proposed for wind speed uncertainty modeling to calculate the hourly output power from wind DG. Afterwards, the values of output power are considered for determining optimal allocation and penetration of wind DG in distribution network to minimize the Average Multi-Objective Index (AIMO) using Particle Swarm Optimization (PSO). The strength of the proposed methodology is validated on IEEE 33 and 69-bus systems. Results depict that, proposed methodology is appropriate for wind speed modeling and is suitable for implementing in power system planning. • Time-varying characteristics of load and generation are assumed for DG allocation. • Weibull distribution-based PG model is proposed to determine hourly wind DG output. • Hourly output power values are considered for the optimal allocation of DG. • The penetration level of wind DG is calculated for different TVVD load models.

Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators

Over the past decades, the deployment of distributed generations (DGs) in distribution systems has grown dramatically due to the concerns of environment and carbon emission. However, a large number of DGs have introduced more uncertainties and challenges into the operation of distribution networks. Due to the stochastic nature of renewable energy resources, probabilistic tools are needed to assist systems operators in analyzing operating states of systems. To address this issue, we develop a probabilistic framework for the assessment of systems. In the proposed framework, the uncertainties of DGs outputs are modeled using short term forecast values and errors. Moreover, an adaptive cluster-based cumulant method is developed for probabilistic load flow calculation. The performance of the proposed framework is evaluated in the IEEE 33-bus system and PG&E 69-bus system. The results indicate that the proposed framework could yield accurate results with a reasonable computational burden. The excellent performance of the proposed framework in estimating technological violations can help system operators underlying the potential risks of systems.

Optimal configuration of the energy storage system in ADN considering energy storage operation strategy and dynamic characteristic

To meet the needs of energy storage system configuration with distributed power supply and its operation in the active distribution network (ADN), establish the dynamics of the all-vanadium redox flow battery energy storage system (BESS). On this basis, an energy storage operation of ADN strategy is proposed to stabilise the power fluctuation of the system. The energy storage configuration model with optimising objectives such as the fixed cost, operating cost, direct economic benefit and environmental benefit of the BESS in the life cycle of the energy is constructed, and the energy storage installation capacity, power and installation position are used as decision variables, which are solved by the dynamic programming algorithm. On the basis of case 33 and case 69 node examples and typical daily load and distributed generations output curve, the simulation analysis is carried out to obtain the optimal configuration result. At the same time, the system tie-line power and the dynamic characteristics of the energy storage system before and after the installation of the energy storage device are obtained. The simulation results show the validity of the model.

A two-stage Microgrid cost optimization considering distribution network loss and voltage deviation

This paper proposes a novel two-stage Microgrid (MG) scheduling methodology to decide optimal location and size of MG and outputs of dispatchable and intermittent distributed generators (DGs). In the first stage, a system stability index (SSI) representing the performance of the distribution network is proposed to select the location of the MG. The size of the MG is derived by minimizing of SSI. In the second stage, the group search optimizer (GSO) algorithm is applied to further determine the outputs of each DG in the MG, including photovoltaic (PV) panels, wind turbines (WT), battery energy storage systems (BESS) and diesel generators (DIG). To validate the proposed approach, the IEEE 33-bus radial distribution systems has been applied. The simulation results show that the approach minimizes MG investment costs under the premise of considering loss and voltage deviation of the distribution network.

Dynamic Reconfiguration of Multiobjective Distribution Networks Considering DG and EVs Based on a Novel LDBAS Algorithm

Aiming to improve the operation economy and power quality of distribution systems subject to the fluctuating and stochastic power outputs of distributed generation (DG) units and electric vehicles (EVs), a multiobjective optimization model for network reconfiguration and its corresponding solution method are proposed. First, a dynamic reconfiguration model is constructed based on the power loss rate (PLR) as well as the active power loss, the load balancing index and the maximum node voltage deviation, which serve as the optimization indexes. Second, the Levy flight and chaos disturbed beetle antennae search (LDBAS) algorithm is presented based on the grey target decision-making technique, which can not only improve the computational efficiency but also find the most satisfactory solution for the proposed dynamic reconfiguration model. Considering the uncertainties of loads and DG outputs, the influence on the load curve of EV connecting to the distribution network at different penetration rates and under different charging/discharging modes is analysed. Additionally, the modified IEEE 33-bus and 118-bus test radial distribution networks are simulated to verify the effectiveness and superiority of the presented reconfiguration model and the improved LDBAS method, and the results illustrate that the proposed reconfiguration method can improve the operation economy and power quality of the distribution system and encourage the penetration of EVs.

Interval State Estimation With Uncertainty of Distributed Generation and Line Parameters in Unbalanced Distribution Systems

Distribution system state estimation (DSSE), which provides critical information for system monitoring and control, is being challenged by multiple sources of uncertainties such as random meter errors, stochastic power output of distributed generation (DG), and imprecise network parameters. This paper originally proposes a general interval state estimation (ISE) model to simultaneously formulate these uncertainties in unbalanced distribution systems by interval arithmetic. Moreover, this model can accommodate partially available measurements of DG outputs and inaccurate line parameters. Further, a modified Krawczyk-operator (MKO) algorithm is proposed to solve the general ISE model efficiently, and effectively provides the upper and lower bounds of state variables under coordinated impacts of these uncertainties. The proposed algorithm is tested on unbalanced IEEE 13-bus and 123-bus systems. Comparison with various methods including Monte Carlo simulation indicates that the proposed algorithm is many orders of magnitude faster and encloses tighter boundaries of state variables.

Stochastic optimal operation of flexible distribution networks for security improvement considering active management

This paper presents a stochastic optimal operation problem of gas turbine integrated distribution networks in the presence of active management schemes, which is formulated as a multi-objective chance-constrained mixed integer nonlinear programming problem. The control variables are the on-load tap-changer tap position, the power provided by the distributed generation (DG), the DG power factor angle, the load participating in demand side management and the switch status. The objectives defined in this paper are to simultaneously minimize the expectation cost and variation coefficient of security distance. Uncertainties related to DG output and load fluctuation and fault power restoration under contingencies are also considered in the optimization problem. The collaboration of normal boundary intersection and dynamic niche differential evolution algorithm is proposed to handle the optimal operation mode. Simulation results are presented and demonstrate the effectiveness of the proposed model. Compared with the operation result without the consideration of security, the security-constrained operation can reduce the expectation cost. So the proposed optimization is reasonable and valuable.

Optimal Volt–Var Curve Setting of a Smart Inverter for Improving Its Performance in a Distribution System

We propose an algorithm to set the parameters of volt-var curves to improve the performance of distributed generation. Specifically, we optimally set the volt-var curves of the smart inverter for proper control of the distributed generation output. To improve the overall distribution system performance, we consider the minimization of voltage deviation, system loss, and peak of reactive power in an objective function, thereby providing optimal volt-var curve parameters. The proposed algorithm overcomes various limitations of existing studies. First, we use distribution system factors in a multiobjective optimization function to adjust volt-var curve parameters that deviate from the default settings. Second, we consider the service transformers that affect the system voltage according to the photovoltaic generation output in the test model during optimization. Third, we analyze the system improvement according to the number of parameters to be optimized. Fourth, we analyze the implementation of the optimized volt-var curve according to the practical periods when the smart inverter settings can be updated. Fifth, we evaluate different suitable volt-var curves for multiple photovoltaic generators grouped using clustering. Using the proposed algorithm, the distribution system operator can set the optimal volt-var curve according to the system conditions and requirements. We conducted simulations of the proposed method using OpenDSS, a quasistatic time-series simulation, on a test model reflecting the characteristics of a South Korean distribution system. The simulation results confirm that the overall system performance increases when the optimal volt-var curve settings obtained from the proposed algorithm are used.

Reliability based optimal DG planning for a meshed distribution network

Distributed generation (DG) sources are becoming more popular nowadays because of their technoeconomic and environmental benefits. These benefits are maximized only when the DG sources are properly planned and installed in the distribution networks. Several studies have been carried out in this field considering various objectives and constraints, for single as well as multiple DG placement and sizing. In this paper, an optimization methodology based on particle swarm optimization is used for the optimal planning of multiple DG sources in a meshed distribution network. The solution consists of the possible DG locations, DG capacities, and its operating power factor. The objectives considered are the improvement of reliability indices namely the system average interruption frequency index and system average interruption duration index, reduction of real power loss, and voltage profile improvement. Unlike other works, reliability evaluation is done considering many realistic constraints and it is used as one of the criteria for DG planning. Reliability indices are evaluated using the encoded Markov cut set algorithm. The real power loss and the voltage index are obtained by means of a simple load flow technique. The stochastic nature of the renewable DG output and the load are taken into account during the reliability evaluation along with islanding probability. The optimal planning of DG sources is done for bus 4 of the Roy billinton test system consisting of seven feeders, 102 nodes, and 106 components. Optimal planning for solar based DG sources is done considering three different load models namely residential, commercial, and mixed load.

Cost-Effective Islanded Electrical System With Decentralized Interleaving PWM for Converter Harmonic Reduction

For a conventional islanded microgrid, each distributed generation (DG) unit usually equips with an LC filter and a closed-loop filter capacitor voltage control is applied to ensure an accurate tracking of DG voltage. In this article, a cost reduction microgrid and DG units configuration is developed via the following two steps. First, an L -type filter is adopted by each DG unit and a centralized shunt capacitor is placed at point of common coupling (PCC). Then, the inherent synchronization feature of droop control is further exploited as an inherent synchronizer to identify the carrier angle relationships between parallel inverters, but without any communications between them. With the carrier information obtained, a decentralized interleaving pulsewidth modulation (PWM) among parallel inverter can be easily achieved via a slow proportional integral (PI) regulation of each DG unit relative carrier. Accordingly, the high-frequency switching harmonics of PCC voltage are suppressed even with simplified and size reduced DG output chokes. Second, a virtual impedance control is applied to DG units to cancel the voltage drops on the DG feeder in a direct PWM manner. Accordingly, mitigating the low-order harmonic PCC voltages is obtained without using any closed-loop voltage tracking at DG unit local processor. Comparative experimental results have been provided to validate the correctness of the proposed method.