天然气网络对含新能源电力系统短期调度运行影响

  [目的]  随着新能源和天然气机组在电力能源系统的装机比例不断增长,天然气系统对含大规模新能源电力系统的短期运行影响需要深入地研究。  [方法]  将天然气网络耦合至电力系统安全约束机组组合模型,以最小化系统运行成本为优化目标,同时考虑新能源接入系统,运用Benders解耦方法求解非线性优化问题。  [结果]  六节点电力系统和六节点天然气网络的综合能源系统算例分析用于验证耦合的优化模型,并表明燃气机组的高爬坡率可帮助电力系统有效地消纳新能源,同时新能源系统可以缓解系统对天然气网络供气约束的依赖。  [结论]  两种能源的优化配置是未来能源系统发展的重要方向。     

基于多Agent技术的现代电力系统机组组合问题研究

针对现代电力系统的新特性，对现代复杂电力系统机组组合问题的目标函数进行了修改和完善．并描述了一种分布式多Agent的解决方案。利用Agent的智能特性。将集中的机组组合规划任务分散到各个独立的供电单元，在减轻中心调度计算负担的同时，也更好地适应了电力市场竞争环境下对机组组合问题提出的利润最大要求。该解决方案虽处于初步探讨阶段。但研究和仿真结果证明能够较好地满足系统和机组双方的约束条件，并获得更大的经济效益。     

考虑RE和PHES的网络约束机组组合优化调度

随着新能源(RE)逐渐并网。网络约束混合能源机组的优化调度研究面临巨大挑战。基于RE并网和抽.水蓄能系统(PHES)耦合作用,研究了网络约束下机组组合(UC)优化调度问题。在网络约束的UC模型中,考虑交流网络约束、环境约束、PHES约束及传统的火电机组约束.利用二进制人工羊群算法解决模型求解问题。将网络约束下UC模型分解成1个主问题和1个子问题,主问题决定机组的开关机状态和负荷分配,子问题检查交流电传输的网络约束。以1个包含风电和光伏机组的IEEE-30节点系统来验证RE和PHES的共同影响,结果表明RE影响了系统的运行成本和电力系统的安全性,PHES对抑制RE的消极影响具有重要作用,能够调节RE的不确定性带来的误差。     

Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling (GS). In this paper, the GS problem is solved to perform the unit commitment (UC) based on frequency prediction by using artificial neural network (ANN) with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff (ABT) signifies the importance of frequency in GS. Under-prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error (APE) and the mean absolute percentage error (MAPE). The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.     

Aggregator-based demand response mechanism for electric vehicles participating in peak regulation in valley time of receiving-end power grid

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles (EVs), an aggregator-based demand response (DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator (ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.     

Long-term optimization of cogeneration systems in a competitive market environment

A tool for long-term optimization of cogeneration systems is developed that is based on mixed integer linear-programming and Lagrangian relaxation. We use a general approach without heuristics to solve the optimization problem of the unit commitment problem and load dispatch. The possibility to buy and sell electric power at a spot market is considered as well as the possibility to provide secondary reserve. The tool has been tested on a demonstration system based on an existing combined heat-and-power (CHP) system with extraction-condensing steam turbines, gas turbines, boilers for heat production and district-heating networks. The key feature of the model for obtaining solutions within reasonable times is a suitable division of the whole optimization period into overlapping sub-periods. Using Lagrangian relaxation, the tool can be applied to large CHP systems. For the demonstration model, almost optimal solutions were found.     

Development of a virtual power market model to investigate strategic and collusive behavior of market players

In this paper, a virtual power market model is proposed to investigate the behavior of power market players from regulator's point of view. In this approach, strategic players are modeled in a multi-agent environment. These agents which are virtual representative of actual players forecast the prices and participate in the markets, exactly the same as real world situation. In addition, the role of ISO is encountered by using security constraint unit commitment (SCUC) and security constraint economic dispatch (SCED) solutions. Moreover, the interaction between market players is modeled using a heuristic dynamic game theory algorithm based on the supply function equilibria (SFE). In addition to the collusive behavior, using the proposed model, the short-term strategic behavior of players, which their effects will appear in long-term, can be simulated.The proposed model enables the market regulators to make decision before implementing new market rules with the confidence of their results. To represent the effectiveness of the proposed method, a case study including wind power plants is considered and the impact of various market rules on players’ behavior is simulated and discussed. Numerical studies indicate that simulating the strategic and collusive behavior prior to any change in the market rules is necessary.     

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES‐integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi‐objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi‐objective optimisation problem is converted into a single‐objective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.     

Active-reactive power scheduling of integrated electricity-gas network with multi-microgrids

Advances in natural gas-fired technologies have deepened the coupling between electricity and gas networks, promoting the development of the integrated electricity-gas network (IEGN) and strengthening the interaction between the active-reactive power flow in the power distribution network (PDN) and the natural gas flow in the gas distribution network (GDN). This paper proposes a day-ahead active-reactive power scheduling model for the IEGN with multi-microgrids (MMGs) to minimize the total operating cost. Through the tight coupling relationship between the subsystems of the IEGN, the potentialities of the IEGN with MMGs toward multi-energy cooperative interaction is optimized. Important component models are elaborated in the PDN, GDN, and coupled MMGs. Besides, motivated by the non-negligible impact of the reactive power, optimal inverter dispatch (OID) is considered to optimize the active and reactive power capabilities of the inverters of distributed generators. Further, a second-order cone (SOC) relaxation technology is utilized to transform the proposed active-reactive power scheduling model into a convex optimization problem that the commercial solver can directly solve. A test system consisting of an IEEE-33 test system and a 7-node natural gas network is adopted to verify the effectiveness of the proposed scheduling method. The results show that the proposed scheduling method can effectively reduce the power losses of the PDN in the IEGN by 9.86%, increase the flexibility of the joint operation of the subsystems of the IEGN, reduce the total operation costs by $32.20, and effectively enhance the operation economy of the IEGN.     

柔性电动汽车和分布式电源的协同优化调度研究

针对电动汽车(EV)接入电网后,出现的负荷"峰上加峰"的问题,在描述传统机组发电特征及调度分布式电源作为补充的基础上,提出了柔性电动汽车与分布式电源协同优化调度方案。该方案考虑4类电动汽车的出行规律及充电方式,同时以发电投资总成本与柔性EV用能成本之和最小作为优化目标,建立车网交互(V2G)的机组组合模型。在考虑柔性EV行驶特性与机组运行特性基础上,将所提模型转化为一个大规模混合整数线性规划(MILP)问题,利用Ipsolve解算器求得最优解以及最优调度方案。文章选取某城市各类电动汽车为研究对象,分析柔性EV用能成本对系统总成本与CO2排放量的影响。仿真结果验证了采用柔性EV与风电协同优化传统机组出力的有效性和考虑柔性EV用能成本的必要性。     

基于RBF神经网络的直流微电网均流控制策略

针对直流微电网功率分配问题,利用径向基神经网络(RBF)建立了直流变换器的动态等效模型,提出了一种改进的功率分配控制策略。以直流微电网模型为基础,以采集的本地单元变换器电压、电流值为输入,以其他单元变换器的输出电压、电流值为输出作为训练数据。在无需通讯的情况下,利用神经网络只需要本单元信息便可准确预测其它并联单元输出,并应用到本地控制器当中,从而改善功率分配和电能质量。文章利用Matlab/Simulink建立的对比仿真模型,证明了该控制策略在多种情况下都具有很好的控制效果。     

改进PSO算法在电力系统机组组合中的运用

采用改进的PSO算法实现在机组组合中的运用,避免了其他算法产生“维数灾”的缺点。通过实例进行仿真计算表明．该方法能够快速的求解．具有有效性和实用性。     

新型节能电力测功器

推荐一种交—直—交三相电压型的双向变流器作电力测功器，它可用于各种动力装置，如汽车发动机、柴油机、汽轮机、内燃机的动力装置的稳态、瞬态及高动态实验，可将所吸收的能量转换成电能回馈到电网，并介绍了此电力测功器的电路拓扑及控制策略。     

A new memetic algorithm approach for the price based unit commitment problem

Unit commitment (UC) is a very important optimization task, which plays a major role in the daily operation planning of electric power systems that is why UC is a core research topic attracting a lot of research efforts. An innovative method based on an advanced memetic algorithm (MA) for the solution of price based unit commitment (PBUC) problem is proposed. The main contributions of this paper are: (i) an innovative two-level tournament selection, (ii) a new multiple window crossover, (iii) a novel window in window mutation operator, (iv) an innovative local search scheme called elite mutation, (v) new population initialization algorithm that is specific to PBUC problem, and (vi) new PBUC test systems including ramp up and ramp down constraints so as to provide new PBUC benchmarks for future research. The innovative two-level tournament selection mechanism contributes to the reduction of the required CPU time. The method has been applied to systems of up to 110 units and the results show that the proposed memetic algorithm is superior to other methods since it finds the optimal solution with a high success rate and within a reasonable execution time.     

Impact of a large penetration of wind generation on the GB gas network

Wind power is expected to be the major element of renewable electricity generation in Great Britain (GB) by 2020 with a capacity of around 30 GW. The potential impact of a large amount of wind generation on the GB gas network was investigated using a combined gas and electricity network model. The varying nature of gas and electric power flows, network support facilities such as gas storage and compressors, and the power ramping characteristics of various power plants were considered. Three case studies were modelled, one case uses the existing network and the other two make use of a hypothesised network in 2020 with two distinct levels of wind generation representing low and high wind periods. The simulation results show that a large penetration of wind generation will influence the electricity generation mix as the wind power varies. Gas-fired generation is used to compensate for wind variability. This will cause increased flows and compressor power consumption on the gas network. Linepack depletion during low wind periods was shown to limit the ability of the gas network to fully supply gas-fired generators.     

Prediction and evaluation of the performance of wind-diesel energysystems

A Monte Carlo based method for predicting the economic performance and reliability of autonomous energy systems consisting of diesel generators and wind energy converters (WECs), is proposed. Several technical constraints are applied, among them the most significant are the limitation of wind power penetration due to both the load demand and a minimum permissible power of the diesel generators. Start up-shutdown costs, two-fuel diesel units and reliability are considered. The proposed method divides the total simulation period into time intervals and for every time interval uses dynamic programming techniques to determine the diesel unit commitment. Results are presented for two Greek islands. It is shown that proper central control of WECs in a system increases significantly the wind energy penetration, which is strongly affected by the way commitment is made     

风电并网研究进展

介绍2011年IEEE/PES学会年会中与风电并网相关的论文,具体研究方向主要包括：风机模型、风电场动态等值、大规模风电并网对电力系统的影响及对策、电压控制和电压穿越技术、机组组合和频率调节、海上风电场等内容。     

考虑风电不确定性的电热系统经济调度

随着风电等新能源大规模并网,其出力的不确定性给电力系统日前调度带来很大挑战。传统的研究方法多是假设风电功率预测误差服从某种概率分布,但风电功率预测误差受到多种因素影响,概率分布模型无法准确描述其特性。为此,采用基于神经网络的组合预测方法对风电功率误差进行建模,再将预测的风电误差加入到包含热电机组、火电机组、风电、储热装置和电锅炉的热电联合优化调度模型中,最后以实际的10机系统为例进行仿真,分析了风电预测误差对机组出力、风电消纳及调度成本的影响。结果表明,与传统方法相比,所建模型可减少机组燃煤成本与旋转备用成本,降低了经济调度成本,提高了风电消纳水平。     

Environmental/economic power dispatch with wind power

Economic environmental dispatch (EED) is a significant optimization problem in electric power system. With more wide spread use of wind power, it is necessary to include wind energy conversion system (WECS) in the EED problem. This paper presents a model to solve the EED problem incorporating wind power. In addition to the classic EED factors, the factors accounting for overestimation and underestimation of available wind power in both economic and environmental aspects are also considered. In order to obtain some quantitative results, the uncertain characteristic of available wind power and the performance of WECS are determined on the basis of the statistical characteristic of wind speed. The optimization problem is numerically solved by a scenario involving two conventional generators and two wind-powered generators. The results demonstrate that the allocation of system generation capacity may be influenced by multipliers related to the cost for overestimation and underestimation of available wind power, and by the multiplier related to the emissions for underestimation of available wind power. Nevertheless, the multiplier related to the emissions for overestimation of available wind power has little impact on the allocation. Taking account of economic factors, environmental factors and impacts of wind power penetration, the proposed EED model is beneficial to finding the right balance between radical and conservative strategy for wind power development.     

Smart house-based optimal operation of thermal unit commitment for a smart grid considering transmission constraints

This paper presents a smart house-based power system for thermal unit commitment programme. The proposed power system consists of smart houses, renewable energy plants and conventional thermal units. The transmission constraints are considered for the proposed system. The generated power of the large capacity renewable energy plant leads to the violated transmission constraints in the thermal unit commitment programme, therefore, the transmission constraint should be considered. This paper focuses on the optimal operation of the thermal units incorporated with controllable loads such as Electrical Vehicle and Heat Pump water heater of the smart houses. The proposed method is compared with the power flow in thermal units operation without controllable loads and the optimal operation without the transmission constraints. Simulation results show the validation of the proposed method.