Optimal Operation of Electronically-Coupled MicroGrids (Dept.E)

The increased diversity of different types of energy sources requires moving towards smart distribution networks. This paper proposes a probabilistic DG (distributed generation) units planning model to determine technology type, capacity and location of DG units while simultaneously allocating ESS (energy storage systems) based on predetermined capacities. This problem is studied in a wind integrated power system considering loads, prices and wind power generation uncertainties. A suitable method for DG unit planning will reduce costs and improve reliability concerns. Objective function is a cost function that minimizes DG investment and operational cost, purchased energy costs from upstream networks, the defined cost to reliability index, energy losses and the investment and degradation costs of ESS. Electrical load is a time variable and the model simulates a typical radial network successfully. The proposed model was solved using the DICOPT solver under GAMS optimization software.

Recent deployment of distributed generation has led to a revolution in distribution systems and the emergence of “smart grid” concepts. The smart grid mainly intends to facilitate integration of renewable energy sources and to achieve higher system reliability and efficiency. Different distributed generation types found in modern distribution systems can be classified into two main categories: intermittent- and dispatchable-based distributed generation; the latter has a unique capability of enabling successful islanding operation, thus improving system reliability. This article proposes a methodology for allocating dispatchable distributed generation units in distribution systems to economically improve system reliability. Distributed generation installation and operation costs are to be optimized against the reliability value, expressed as customers’ “willingness to pay” (WTP) to avoid power interruptions. Therefore, the main goal of this research work is to determine the optimal combination between distributed generation units to be installed and loads to be shed during all possible contingencies. A probabilistic load model is adopted to determine the optimal size of allocated distributed generation units.

To manage the microgrid effectively, it is important to get the power information among multiple distributed generation (DG) units. However, when the droop control method is adopted, as the corresponding DG units cannot be simply modeled as VF or PQ buses, the conventional power flow algorithm may become inapplicable. To solve this issue, a time domain iteration (TDI) based power flow algorithm is hereby proposed. Firstly, a microgrid model is prepared, containing a network model and several DG unit models. Then, the proposed TDI is executed for power flow calculation, which mimics the real-time operation of microgrids. In each iteration, the DG unit models input voltages and currents to the network model; then the network model changes its state accordingly and feeds related parameters back to those DG unit models. As the DG unit models simulate the behavior of actual DG units, the proposed algorithm is not limited to the droop control governed microgrids. Moreover, as the TDI has definite physical meaning, convergence problems can be avoided. Finally, the validity of the proposed power flow algorithm is verified through the Matlab simulation results from an 8-bus microgrid system.

This paper describes a control method for distributed generation (DG) units to implement active power sharing and frequency recovery simultaneously in an islanded microgrid. Conventional active power–frequency ( P – f ) droop control is used for the DG controller, and the frequency deviation is recovered by the DG itself via self-frequency recovery control, without requiring secondary frequency control. Because the electrical distance (impedance) from each DG unit to a point where the load demand changes differs among DG units, the instantaneous frequency deviations may differ between DG units. These differences are fed into the integrators of the self-frequency recovery control and may result in errors in active power sharing. To solve this problem and share active power more accurately, a compensation control method is developed for active power sharing, which considers the droop coefficients of each of the DG units. Simulation results show that the proposed control method is effective.

The impact of distributed generation (DG) on distribution power losses is closely related to DG technologies and load distribution. DG units are modeled as PQ, PQ(V) and PV nodes in power flow calculation. A revised back/forward power flow method is employed to calculate distribution power flow with various types of DG units. Power losses are computed when different types of DG units are connected to heavy load, general load and light load area apart. Simulation on IEEE 33-bus distribution network shows that connecting DG to heavy load area helps to maximize the loss reduction. And the installation of DG consuming reactive power will increase network losses unless reactive power compensation device is installed accordingly.

In this paper, a control strategy for a distributed generation (DG) unit that provides a seamless transition between grid connected mode and islanded mode is presented. Instead of switching between current control and voltage control modes as commonly proposed in the literature, a voltage control mode is used in both grid connected and standalone modes. The output voltage and frequency are controlled to regulate the reactive and real power injected into the grid, respectively. In the islanded mode, the output voltage and frequency of the DG unit are regulated at their nominal values. Therefore, transition between operation modes is achieved by switching the voltage and frequency references of the same voltage controller. When the grid is interrupted, the DG unit supplies only the load automatically with no abrupt transients in the local load voltage and current, even before the islanding is detected. The principle and implementation of the control strategy to detect the grid conditions and islanding, and to switch seamlessly between the two operating modes are developed. The control strategy is validated through simulations using a detailed switching models in the PSCAD/EMTDC environment.

This paper develops a model for distribution network expansion planning (DNEP) considering the reliability issues. With simultaneous switch placement and DNEP, the proposed model tries to adequately consider the reliability issues in the planning stage in a way that the designed network has suitable performance from the reliability viewpoint. Reinforcement or installation of MV feeders and HV/MV substations have been regarded as the planning options. Moreover, the distributed generation (DG) is taken into account as another alternative in the DNEP. By considering the equipment's installation and operation costs, the model seeks for an optimal combination of feeders, substations, DGs, and sectionalizing switches in order to reliably supply the loads of network in the horizon year with the minimum cost subject to the technical and operational constraints. In the developed model, the DG units are allowed to be operated in the islanding mode in order to enhance the network reliability. The presented approach has been modeled as an optimization problem where a GA-OPF method is employed to solve it. Several experiments have been set up to examine and justify capabilities of the proposed approach.

<span>This paper proposes a new optimal operation of Microgrids (MGs) in a distribution system with wind energy generators (WEGs), solar photovoltaic (PV) energy systems, battery energy storage (BES) systems, electric vehicles (EVs) and demand response (DR). To reduce the fluctuations of wind, solar PV powers and load demands, the BES systems and DR are utilized in the proposed hybrid system. The detailed modeling of WEGs, solar PV units, load demands, BES systems and EVs has been presented in this paper. The objective considered here is the minimization of total operating cost of microgrid, and it is formulated by considering the cost of power exchange between the main power grid and microgrid, cost of wind and solar PV energy systems, cost of BES systems, EVs and the cost due to the DR in the system. Simulations are performed on a test microgrid, and they are implemented using GAMS software. Various case studies are performed with and without considering the proposed hybrid system.</span>

One of the important issues of microgrid development is how to optimize the operation of the microgrid that contains different kinds of distributed generation. This paper presents the application of particle swarm algorithm, based on chaotic simulated annealing to optimize the operation of the microgrid. Considering the operation cost, environmental benefits and other performance indicators as the objective function, to establish the mathematical model of microgrid optimal operation, propose the method of microgrid optimal operation based on chaotic simulated annealing particle swarm algorithm, and applied it to solve the multi-objective and multi-constrained problems of microgrid optimal operation. The example analysis proved the effectiveness and feasibility of this algorithm.

Under the new round of power market reform policy, strengthening the life cycle economic evaluation of power grid projects is the key to speeding up the transformation and upgrading of power grids and improving investment efficiency. Considering that the operation and maintenance costs of power grid is unified accounting, how to calculate the operation costs of single grid project scientifically and rationally is the key to conduct economic evaluation. Firstly, the multi-factor relationship between grid operation costs and the substation capacity, line length, transmission and distribution volume and fixed asset value is analyzed to determine the key factors. Then, the operation and maintenance costs allocation model of grid project based on Performance Value is constructed in this paper. Finally, the operation data of a provincial power grid company is selected as a sample to calculate the operation and maintenance costs of different power grid projects, which verifies the effectiveness of the model.

Enzymatic reactor selection and derivation of the optimal operation policy, by using a model-based modular simulation platform

This paper presents modeling, active power management strategy, and implementation of a fuel cell, pbotovoltaic, and super-capacitor power conversion system in MATLAB. The micro-grid includes two distributed generation (DG) units. Each DG unit consists of a fuel cell (FC), photovoltaic unit (PV) and super-capacitor (SC). The super-capacitor energy storage compensates the shortage power of FC stack, PV unit and the load demand. The output of DG unit is connected to grid using inverter. Load changes are applied for balanced conditions. The frequency deviation is fed to adaptive proportional resonance controller which adapts to the changes in frequency. This results in eliminating steady state error and output impedance of DG unit is decreased thereby reducing low frequency oscillations in the power components of the DG units. The system is modeled and simulated using software. The dynamic response of the DG units to balanced load changes is investigated using software.

A novel two-phase approach to manage the daily operation of proton exchange membrane (PEM) fuel cells for residential applications is presented in this paper. Conventionally, the performance optimization is carried out offline since it is a time-consuming process and needs high computational capabilities. To simplify the management process and to enable online parameter updating, the paper suggests a new technique using artificial neural networks (ANNs). First, a database is extracted by performing offline optimization processes at different load demands and natural gas and electricity tariffs using a genetic algorithm (GA). Then, the obtained results are used for the offline training and testing of the ANN, which can be used onsite to define the settings of the fuel cell. The tariffs and load demands as inputs of the ANN can be easily updated online to enable the ANN to estimate new optimal or quasioptimal set points after each variation in operating points. The agreement between ANN decisions and optimal values as well as the achieved reduction in operating costs encourage the implementation of the proposed technique to achieve both fast online adaptation of settings and near optimal operating cost. This technique is applicable for different distributed generating units (DGUs), which are expected to spread within the power systems in the near future.

For pumping stations installed blade full-adjustable pumps with frequently varying head in a wide range, adjusting pump blade angles is an effective way to realize optimum operation. Optimal mathematical models were established aiming at the least daily operation cost for pumping station system, which includes main pump units, inlet and outlet passages and their accessories, forebay and outlet pond, auxiliary equipment, power transmission and transformation equipment, other electromechanical equipments and lightings. Also, head varying, time-varying electrical price, and blade adjusting frequency are all taken into account. Meanwhile, the influences of the blade adjusting frequency on operation cost are researched. The results indicate that the operation cost is reduced by 5.64%∼12.13% by the optimal schemes considering time-varying electrical price and adjusting pump blade angles than that of conventional schemes, taking Jiangdu Forth Pumping Station in China as a case. The more the blade adjusting frequency, the less the operation cost. Also, if the blade adjusting frequency exceeds eight times, the optimum operation cost is close to be a const. Therefore, it is advisable to adjust blade angles four to six times a day to save operation cost and guarantee the reliability of pump units.

Considering the fact that the optimal operation of micro grid is an economic dispatch of a dynamic system, the study investigates the establishment of the dynamic optimization model for optimally operating micro grid and attempts to solve the model. In view of the significance of the economic operation, for optimally determining the power outputs of each micro source and the power import from/export to the grid, a multi-objective dynamic optimization model with physical, technical, and operational constraints is established to minimize the operation & maintenance cost, maximize the environmental benefits, and minimize the depreciation cost. To deal with the nonlinear optimization problem with high-dimension decision variables and various constraints, an improved particle swarm optimization algorithm (IPSO) is proposed and applied to the optimal operation of a micro grid case. Simulation results reveal the advantage of the proposed dynamic optimization model and the effectiveness of applying IPSO.