Showing papers on "Power system simulation published in 2018"

PyPSA: Python for Power System Analysis

Abstract: Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and optimising modern electrical power systems over multiple periods. PyPSA includes models for conventional generators with unit commitment, variable renewable generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. It is designed to be easily extensible and to scale well with large networks and long time series. In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations. PyPSA is positioned in the existing free software landscape as a bridge between traditional power flow analysis tools for steady-state analysis and full multi-period energy system models. The functionality is demonstrated on two open datasets of the transmission system in Germany (based on SciGRID) and Europe (based on GridKit). Funding statement: This research was conducted as part of the CoNDyNet project, which is supported by the German Federal Ministry of Education and Research under grant no. 03SF0472C. The responsibility for the contents lies solely with the authors

Two-Stage Optimization of Battery Energy Storage Capacity to Decrease Wind Power Curtailment in Grid-Connected Wind Farms

Abstract: As wind power makes an increasing contribution to power systems, the problems associated with wind power curtailment have become a concern in recent years. Battery energy storage (BES) can reduce the effects of wind power curtailment by peak shaving and wind power forecast error compensation. Accordingly, the operational constraints of power systems and wind power uncertainty should be considered in the optimization of BES capacity installed at wind farms. This paper proposes a two-stage method to determine the optimal power and capacity of BES in systems including thermal plants, wind farms, and BES. In the first stage, the unit commitment of thermal generators and scheduled wind farm output are optimized with ac power flow constraints modeled by second-order cone programming. Time series of the wind farm output generated by Monte Carlo simulations are used for BES optimization. In the second stage, operational strategies for BES are designed. The simulation results indicate that cooperation between the BES and wind farm using the proposed method can reduce the costs of both wind farms and thermal plants. Finally, a sensitivity analysis is performed to assess the influence of the BES unit cost and wind power penetration on the optimal power and capacity of BES.

Robust Optimal Operation of AC/DC Hybrid Microgrids Under Market Price Uncertainties

Abstract: Energy management system (EMS) is responsible for the optimal operation of microgrids. EMS adjusts its operational schedule for near future by using the available information. Market price signals are generally used for the operation of microgrids, which are obtained by using estimation/ forecasting methods. However, it is difficult to precisely predict the market prices due to the involvement of various complex factors like weather, policy, demand, errors in forecasting methods, and fuel cost. Therefore, in this paper, the uncertainties associated with the real-time market price signals (buying and selling) are realized via a robust optimization method. In addition to market price signals, uncertainties associated with renewable power sources and forecasted load values are also considered. Initially, a deterministic model is formulated for an ac/dc hybrid microgrid. Then a min–max robust counterpart is formulated by considering the worst-case uncertainties. Finally, an equivalent mixed integer problem is formulated by using linear duality and other optimality conditions. The developed model can provide feasible solutions for all the scenarios if the uncertainties fluctuate within the specified bounds. The effect of market price uncertainties on internal power transfer and external power trading, operation cost, the state-of-charge of energy storage elements, and unit commitment of dispatchable generators is analyzed. Taguchi’s orthogonal array (OA) method is used to find the worst-case scenario within the specified uncertainty bounds. Then, Monte Carlo method is used to generate various scenarios within the uncertainty bounds to evaluate the robustness of the selected scenario via Taguchi’s OA method. Finally, a violation index is formulated to evaluate the robustness of the proposed approach against the deterministic model. Simulations results have validated the robustness of the proposed optimization strategy.

Switch 2.0: A Modern Platform for Planning High-Renewable Power Systems

Abstract: This paper describes Switch 2.0, an open-source modeling platform for planning transitions to low-emission electric power grids, designed to satisfy 21st century grid planning requirements. Switch is capable of long-, medium- and short-term planning of investments and operations with conventional or smart grids, integrating large shares of renewable power, storage and/or demand response. Applications include integrated resource planning, investment planning, economic and policy analyses as well as basic research. Potential users include researchers, educators, industry and regulators. Switch formulates generation and transmission capacity planning as a mixed integer linear program where investment and operation are co-optimized across sampled time series during multiple investment periods. High-resolution production cost modeling is supported by freezing investment decisions and including longer time series and more operational details. Modeling features include unit commitment, part-load efficiency, planning and operating reserves, fuel supply curves, storage, hydroelectric networks, policy constraints and demand response. Switch has a modular architecture that allows users to flexibly compose models by choosing built-in modules 'a la carte' or writing custom modules. This paper describes the software architecture and model formulation of Switch 2.0 and provides a case study in which the model was used to identify the best options for obtaining load-shifting and reserve services from batteries and demand response in a 100% renewable power system.

Stochastic Unit Commitment With Topology Control Recourse for Power Systems With Large-Scale Renewable Integration

Abstract: In this paper, we model topology control through transmission switching as a recourse action in the day-ahead operation of power systems with large-scale renewable generation resources. We prove that transmission switching could only reduce the linear objective value of direct current optimal power flow when congestion exists. However, we also show, through a simple example, that unit commitment cost could be reduced by transmission switching even in the absence of congestion. To solve the stochastic unit commitment with topology control within reasonable computational time, we proposed a heuristic that first decomposes a practical system into zones and then solves the problem for each zone in parallel. The benefit of topology control recourse is demonstrated on a network representing the Central European System. We compare the costs of the network with different loading and renewable generation conditions. The cost reduction of the test system can reach 3.34% with heavy load and large-scale renewable generation while in a single zone the cost reduction can be above 7%.

Distributed Dispatch Approach for Bulk AC/DC Hybrid Systems With High Wind Power Penetration

Abstract: For bulk AC/DC hybrid transmission systems with high wind power penetration, this paper presents a distributed dispatch approach to the security-constrained unit commitment (SCUC) problem. To fully use the flexible adjustment capability of the HVDC tie-line to promote inter-regional wind power accommodation, an operation model for the HVDC tie-line is presented. Then, a distributed SCUC approach based on the analytical target cascading technique is proposed, where the day-ahead SCUC problem is decomposed into an upper-level master problem and parallel subproblems of lower level regional dispatch. The master problem is in charge of determining the day-ahead transmission plan for the HVDC tie-line, and the lower-level dispatch centers independently solve their SCUC problems in parallel in accordance with the hierarchical and partitioned power scheduling mode. Simulations show that considering the flexible and controllable nature of the HVDC tie-line, the proposed distributed dispatch approach for bulk AC/DC hybrid systems has the advantages of promoting inter-regional wind power accommodation and improving the economics of the overall system operation while relieving the peak regulation pressure of the receiving-side system.

Unit Commitment Problem in Electrical Power System: A Literature Review

Abstract: Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.

Modelling and allocation of open-UPQC-integrated PV generation system to improve the energy efficiency and power quality of radial distribution networks

Abstract: The study presents the modelling and allocation strategy for open unified power quality conditioner (UPQC-O) integrated photovoltaic (PV) generation system in radial distribution networks to improve the energy efficiency and PQ. An UPQC is a custom power device, which consists of series and shunt inverters. In UPQC-O, these inverters are placed in different locations in a network. There is a communication channel to share the information among these inverters to select the respective set point. Two models proposed are: (i) UPQC-O with battery and PV array (UPQC-O-WB) and (ii) UPQC-O with only PV array (UPQC-O-WOB). In UPQC-O-WB, the energy generated by PV array is stored during its operation hour to utilise it during peak hour. However, in UPQC-O-WOB, the energy generated by PV array is directly injected to the network. The proposed models are incorporated in the forward-backward sweep load flow to determine the operational parameters such as bus voltage. An optimisation problem is formulated to determine the optimal placement of UPQC-O with PV array in distribution networks. The objective function includes the investment and operational costs of inverters, battery and PV array, and the cost of energy loss. The particle swarm optimisation is used as the solution strategy.

Temporal Decomposition for Improved Unit Commitment in Power System Production Cost Modeling

Abstract: Long-term planning in power systems requires simulations of unit commitment (UC) for long time periods up to 20 years. Such simulations are conducted with production cost models (PCMs), which involve solving large-scale mixed-integer programming (MIP) problems with a large number of variables and constraints, because of the long planning horizon. We have developed new optimization modeling and solution techniques based on a decomposition scheme to reduce the solution time and improve the accuracy in PCMs. We propose a temporal decomposition that solves the UC problem by systematically decoupling the long-horizon MIP problem into several subhorizon models. The decomposition is obtained by the Lagrangian relaxation of the time-coupling constraints such as ramping constraints and minimum uptime/downtime constraints. The key challenge is to solve several sub-MIP problems while effectively searching for dual variables to accelerate the convergence of the algorithm. We implement the temporal decomposition in an open-source parallel decomposition framework, which can solve the multiple subproblems in parallel on high-performance computing clusters. We also implement the branch-and-bound method on top of the decomposition in order to find a primal optimal solution. Numerical results of the decomposition method are reported for the IEEE 118-bus and PEGASE 1354-bus test systems with up to an 168-hour time horizon.

A 400-V/50-kVA Digital–Physical Hybrid Real-Time Simulation Platform for Power Systems

Abstract: Power system simulation is an important means to study the dynamic behavior, to ensure the safety and stability, and to optimize the operation of a power grid. Pure physical simulation and digital simulation have their own advantages and disadvantages. A three-phase four-wire hybrid simulation platform integrating the advantages of both the digital simulation and physical simulation is developed by combining the physical simulation system and real-time digital simulator. The platform is rated at 400 V and 50 kVA with the short-circuit capacity of 500 kVA and can supply ten times the rated current to support the simulation of various short-circuit faults. An improved interface algorithm based on an ideal transformer model is proposed to extend the stability region of a hybrid simulation. Hybrid simulation experiments are conducted under two cases, and the comparison with the digital simulation demonstrates the performance of this platform and the potential applied to a modern power system. This platform has been equipped in a key laboratory of smart power grid technology.

A Dynamic Regrouping Based Dynamic Programming Approach for Unit Commitment of the Transmission-Constrained Multi-Site Combined Heat and Power System

Abstract: Combined heat and power (CHP) systems offer additional advantage and flexibility for addressing power grid balance resulting from large-scale introduction of intermittent renewable energy sources (RES) in contrast to power-only systems. The dependence between heat and power production in the CHP plant can be utilized to adjust power production level to accommodate more RES. Furthermore, electricity can be transformed into heat by electric heater and heat pump to avoid starting up heat led CHP plants when RES production is abundant. This paper focuses on solving efficiently unit commitment of the interconnected multi-site CHP system without considering RES. A relaxed on/off state based dynamic programming applying sequential commitment scheme in conjunction with dynamic regrouping is used to coordinate heat and power production in each site (region) as well as power transmission across sites. Computational experiments for real-life daily scheduling demonstrate that our method generates solutions much more quickly than a standard high-performance optimizer (CPLEX) with comparable solution quality, and lays foundation for the future handling of uncertainties of intermittent RES.

Coordinated Control Strategy of a Battery Energy Storage System to Support a Wind Power Plant Providing Multi-Timescale Frequency Ancillary Services

Abstract: With increasing penetrations of wind generation on electric grids, wind power plants (WPPs) are encouraged to provide frequency ancillary services (FAS); however, it is a challenge to ensure that variable wind generation can reliably provide these ancillary services. This paper proposes using a battery energy storage system (BESS) to ensure theWPPs’ commitment to FAS. This method also focuses on reducing the BESS’s size and extending its lifetime. In this paper, a state-machine-based coordinated control strategy is developed to utilize a BESS to support the obliged FAS of a WPP (including both primary and secondary frequency control).This method takes into account the operational constraints of theWPP (e.g., real-time reserve) and the BESS (e.g., state of charge [SOC], charge and discharge rate) to provide reliable FAS. Meanwhile, an adaptive SOC-feedback control is designed to maintain SOC at the optimal value as much as possible, and, thus, reduce the size and extend the lifetime of the BESS. The effectiveness of the control strategy is validated with an innovative multi-area interconnected power system simulation platform that can mimic realistic power systems operation and control by simulating realtime economic dispatch, regulating reserve scheduling, multi-area automatic generation control, and generators’ dynamic response.

Steady state and transient simulation for electricity-gas integrated energy systems by using convex optimisation

Abstract: The increasing interdependence between electric power and natural-gas systems requires integrated simulation tools applicable to the coupled system. Therefore, this study proposes steady-state and transient simulation tools for electricity-gas integrated energy systems (IESs) by using convex optimisation. Steady-state IES simulation is designed for long-term planning studies while transient IES simulation, which includes line-pack storage, is designed for real-time operational research. The proposed convex steady-state gas flow model introduces binary variables to represent gas flow directions, and employs the second-order cone (SOC) to relax non-convex pipeline gas flow equations. As a result, steady-state gas flow analysis is formulated as a mixed integer SOC programming problem. The proposed transient gas flow model employs the implicit finite difference method to transform partial differential equations into algebraic difference equations. Moreover, the short-term gas flow directions are specified and SOC relaxations are used. Simulation results on an integrated IEEE 24-node system and Belgian 20-node gas system verify the effectiveness of the proposed SOC models. The impacts of wind power forecast errors on multi-period gas network operations are also investigated.

A Comprehensive Approach for Maximizing Flexibility Benefits of Electric Vehicles

Abstract: Increasing variability and uncertainty coming from both sides of the power system equilibrium equation, such as wind energy on the generation side and increasing share of new consumers such as electric vehicles on the demand side, entail higher reserve requirements. While traditional approaches of assigning conventional generation units to maintain system stability can increase operational costs, greenhouse gas emissions, or give signals for new investments, utilizing intelligent control of distributed sources might mitigate those negative effects. This can be achieved by controllable charging of domestic electric vehicles. On the other hand, increasing number of public charging stations gives final users the opportunity to fast charge, making their vehicles an additional source of uncertainty rather than a provider of flexibility. This paper brings a full system assessment of combined effect of slow home charging of electric vehicles together with fast charging stations (both with and without integrated energy storage systems), cast as mixed integer linear programming unit commitment model. The contributions of this paper look into optimal periods when fast charging is beneficial for the system operation, as well as assess the benefits of integrating battery storage into fast charging stations to mitigate the negative effects to power system operation.

Unit Commitment for Isolated Microgrids Considering Frequency Control

Abstract: This paper presents a mathematical model of frequency control in isolated microgrids, which is integrated into the Unit Commitment (UC) problem. In conventional UC formulations, power outputs are considered fixed between two periods, yielding a staircase pattern with respect to the energy balance of the generation and demand for a typical dispatch time horizon (e.g., 24 h). However, in practice generation units that participate in frequency control may see a change in their output within a single dispatch time interval (e.g., 5 min), depending on the changes in the demand and/or renewable generation. The proposed approach considers these changes in the generation output using a linear model, and based on that, a novel UC mixed integer quadratic programming, with linear constraints and quadratic objective function, is developed which yields a more cost efficient solution for isolated microgrids. The proposed UC is formulated based on a day-ahead with model predictive control approach. To test and validate the proposed UC, a modified version of a CIGRE benchmark test system is used. The results demonstrate that the proposed UC would reduce the operational costs of isolated microgrids compared to conventional UC methods, at similar complexity levels and computational costs.