Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power

The current energy consumption in most of the countries is weighing heavily on fossil fuels, which account for about 70–90% of total energy used. The ecological concerns about air pollution and global warming are encouraging wider use of clean renewable technologies such as wind and solar energy. In this paper, Gbest guided artificial bee colony algorithm (GABC) is applied to optimize the emission and overall cost of operation of wind–thermal power system. The random nature of wind power is modeled using weibull probability distribution function (PDF). Moreover, the uncertainty in wind power is considered in the cost model by including the power imbalance terms such as overestimation and underestimation costs of available wind power. To validate the effectiveness of proposed method, it is first applied to three standard test systems considering different technical constraints such as valve loading effect, prohibited zones, ramp rate limits, etc. In second part, the effect of wind power generation on dispatch cost and emission is analyzed for IEEE-30 bus test system. A comparative analysis with other similar optimization techniques reveals that the proposed technique has better solution accuracy and convergence results.     

基于滑模控制的含风储多域电力系统负荷频率控制

建立含风储多域互联电力系统负荷频率控制(LFC)模型,同时考虑系统参数不确定性、储能系统和传统机组控制信道延时问题.为提高系统鲁棒性,降低储能系统的容量配置,针对含风储的LFC模型,设计滑模负荷频率控制器,并提出滑模负荷频率控制器和储能协调的控制策略.算例分析表明,所提出的协调控制策略在新能源大规模渗透和系统负荷波动情况下能够有效减小系统频率偏差和区域控制偏差,同时降低储能系统的配置容量,提高电力系统安全稳定运行的经济性.     

An application of model predictive control to the dynamic economic dispatch of power generation

Two formulations exist for the problem of the optimal power dispatch of generators with ramp rate constraints: the optimal control dynamic dispatch (OCDD) formulation based on control system models, and the dynamic economic dispatch (DED) formulation based on optimization. Both are useful for the dispatch problem over a fixed time horizon, and they were treated as equivalent formulations in literature. This paper first shows that the two formulations are in fact different and both formulations suffer from the same technical deficiency of ramp rate violation during the periodic implementation of the optimal solutions. Then a model predictive control (MPC) approach is proposed to overcome such a technical deficiency. Furthermore, it is shown that the MPC solutions, which are based on the OCDD framework, converge to the optimal solution of an extended version of the DED problem and they are robust under certain disturbances and uncertainties. Two standard examples are studied: the first one of a ten-unit system shows the difference between the OCDD and DED, and possible ramp rate violations, and the second one of a six-unit system shows the convergence and robustness of the MPC solutions, and the comparison with OCDD as well.     

Capacity expansion of stochastic power generation under two-stage electricity markets

Energy imbalances due to power forecast errors have a significant impact on both the cost of operating the power system and the profitability of stochastic power generating units. In this paper, we propose a modeling framework to analyze the effect of the costs of these imbalances on the expansion of stochastic power generating units. This framework includes the explicit representation of a day-ahead and a balancing market-clearing mechanisms to properly capture the impact of forecast errors of power production on the short-term operation of a power system. The proposed generation expansion problems are first formulated from the standpoint of a social planner to characterize a perfectly competitive market. We investigate the effect of two paradigmatic market designs on generation expansion planning: a day-ahead market that is cleared following a conventional cost merit-order principle, and an ideal market-clearing procedure that determines day-ahead dispatch decisions accounting for their impact on balancing operation costs. Furthermore, we reformulate the proposed models to determine the optimal expansion decisions that maximize the profit of a collusion of stochastic power producers in order to explore the effects of competition at the investment level. The proposed models are first formulated as multi-level programming problems and then recast, under certain assumptions, as single-level mixed-integer linear or non-linear optimization problems using duality theory. The variability of the forecast of the stochastic power production and the demand level throughout the planning horizon is modeled using yearly duration curves. Likewise, the uncertainty pertaining to power forecast errors is characterized through scenario sets. The main features and results of the proposed models are discussed using an illustrative example and a more realistic case study based on the Danish power system.     

Minimum energy storage for power system with high wind power penetration using p-efficient point theory

Minimum energy storage （ES） and spinning reserve （SR） for day-ahead power system scheduling with high wind power penetration is significant for system operations. A chance-constrained energy storage optimization model based on unit commitment and considering the stochastic nature of both the wind power and load demand is proposed. To solve this proposed chance-constrained model, it is first converted into a deterministic-constrained model using p-efficient point theory. A single stochastic net load variable is developed to represent the stochastic characteristics of both the wind power and load demand for convenient use with the p-efficient point theory. A probability distribution function for netload forecast error is obtained via the Kernel estimation method. The proposed model is applied to a wind-thermal-storage combined power system. A set of extreme scenarios is chosen to validate the effectiveness of the proposed model and method. The results indicate that the scheduled energy storage can effectively compensate for the net load forecast error, and the increasing wind power penetration does not necessarily require a linear increase in energy storage.     

求解电力系统经济调度问题的改进粒子群优化算法

电力系统经济调度问题是电力系统中的一个重要的研究课题,针对该问题,提出一种改进粒子群优化(ODPSO)算法.改进算法在搜索前期,采用广义的反向学习策略,使算法能够快速地靠近较优的搜索区域,从而提高收敛速度;在搜索后期,借鉴差分进化算法的进化机制设计改进的变异和交叉策略,对当前种群的最优粒子进行更新,从而提高种群的多样性,进而协助算法获得全局最优解.为了验证改进粒子群优化算法的有效性,对CEC2006提出的22个基准约束测试函数进行仿真,结果表明改进算法相比其他算法在寻优精度和稳定性上更具优势.最后,将改进算法应用于考虑机组爬坡速率约束、机组禁行区域约束以及电力平衡约束的两个电力系统经济调度问题,取得了令人满意的结果.     

智能水滴算法与SQP相混合的电力环境经济调度

电力环境经济调度对于降低发电过程中煤耗成本和污染气体排放有着重要意义。本文给出一种智能水滴算法（intelligent water drops,IWD）和序列二次规划（sequential quadratic programming,SQP）相混合求解电力环境经济调度问题的方法（IWD-SQP）。针对SQP全局搜索弱的缺点,将智能水滴算法应用于求解连续优化问题,同时将每次迭代过程中水滴所产生的解作为序列二次规划初始解进行微调以得到更好的解。将提出的方法应用于一个10机组测试系统进行实验,与其他方法求解考虑阀点效应的电力环境经济调度问题相比,验证了IWD-SQP的可行性和有效性。     

Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm

This paper presents the hybrid harmony search algorithm with swarm intelligence (HHS) to solve the dynamic economic load dispatch problem. Harmony Search (HS) is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work is an attempt to hybridize the HS algorithm with the powerful population based algorithm PSO for a better convergence of the proposed algorithm. The main aim of dynamic economic load dispatch problem is to find out the optimal generation schedule of the generators corresponding to the most economical operating point of the system over the considered timing horizon. The proposed algorithm also takes care of different constraints like power balance, ramp rate limits and generation limits by using penalty function method. Simulations were performed over various standard test systems with 5 units, 10 units and 30 units and a comparative study is carried out with other recently reported results. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.     

A new wind power prediction method based on ridgelet transforms,hybrid feature selection and closed-loop forecasting

To reduce network integration and boost energy trading, wind power forecasting can play an important role in power systems. Furthermore, the uncertain and nonconvex behavior of wind signals make its prediction complex. For this purpose, accurate prediction tools are needed. In this paper, a ridgelet transform is applied to a wind signal to decompose it into sub-signals. The output of ridgelet transform is considered as input of new feature selection to identify the best candidates to be used as the forecast engine input. Finally, a new hybrid closed loop forecast engine is proposed based on a neural network and an intelligent algorithm to predict the wind signal. The effectiveness of the proposed forecast model is extensively evaluated on a real-world electricity market through a comparison with well-known forecasting methods. The obtained numerical results demonstrate the validity of proposed method.     

Wind power ramp event detection with a hybrid neuro-evolutionary approach

In this paper, a hybrid system for wind power ramp events (WPREs) detection is proposed. The system is based on modeling the detection problem as a binary classification problem from atmospheric reanalysis data inputs. Specifically, a hybrid neuro-evolutionary algorithm is proposed, which combines artificial neural networks such as extreme learning machine (ELM), with evolutionary algorithms to optimize the trained models and carry out a feature selection on the input variables. The phenomenon under study occurs with a low probability, and for this reason the classification problem is quite unbalanced. Therefore, is necessary to resort to techniques focused on providing a balance in the classes, such as the synthetic minority over-sampling technique approach, the model applied in this work. The final model obtained is evaluated by a test set using both ELM and support vector machine algorithms, and its accuracy performance is analyzed. The proposed approach has been tested in a real problem of WPREs detection in three wind farms located in different areas of Spain, in order to see the spatial generalization of the method.

     

Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects

Abstract

In this study, symbiotic organisms search (SOS) algorithm is proposed to solve the dynamic economic dispatch with valve-point effects problem, which is one of the most important problems of the modern power system. Some practical constraints like valve-point effects, ramp rate limits and prohibited operating zones have been considered as solutions. Proposed algorithm was tested on five different test cases in 5 units, 10 units and 13 units systems. The obtained results have been compared with other well-known metaheuristic methods reported before. Results show that proposed algorithm has a good convergence and produces better results than other methods.     

Optimal capacitor allocation in power systems

Static capacitors are installed at specific locations in large power systems for minimising power losses and to ensure quality of the supply system. It has been found that the number of units in each capacitor bank can be considerably reduced by suitable choice of transformer tap settings and generator voltage magnitudes, thereby reducing the cost of installation of capacitors. This paper presents an efficient method for finding the optimal values for the number of units in each capacitor bank and their locations for the purpose of reducing power losses and for ensuring the quality of the supply system. This is accomplished by optimal allocation of all reactive power sources in the system in a coordinated fashion by varying the number of units in each capacitor bank and generator voltage magnitudes and transformer tap setting positions.The problem is formulated as an optimisation problem with the objective function representing the revenue resulting from reduction in power losses, taking into account the cost due to interest and depreciation on the static capacitor installation and the constraints based on physical and technical limitations on the system. The problem falls in the category of Mixed Integer Linear Programming (M.I.L.P.) and is solved by decomposing the problem into two smaller subproblems viz., a pure integer programming problem in binary variables and a linear programming problem. The solutions of these subproblems are coordinated to get the solution of the original problem. Very fast convergence is obtained by preventing zigzagging of the solution about the optimal point. All these result in considerable savings in computer time and memory. The proposed model has been applied to sample systems and the results presented and discussed.     

含风电电力系统的主从博弈经济调度

风力发电具有显著的随机性和波动性,对电力系统原有调度模式提出挑战.采用鲁棒优化处理风电不确定性,利用鲁棒优化蕴含的博弈思想,将风电场看作调度中心的一个虚拟博弈者,利用双层规划法建立了二者的主从博弈模型,将调度中心看作领导层,其决策目标为电网运行的成本最低,将风电场看作下属层,其决策目标是能保证系统实时安全运行的最大风电出力区间.由于考虑了火电机组的阀点效应,主从博弈模型呈现出非线性双层规划的数学特点,提出一种改进教与学算法与线性规划相嵌套的求解方法.最后,采用改进的10机39节点系统对模型以及求解方法的有效性进行了验证.     

An approach to on-line power dispatch with ambient air pollution constraints

An on-line optimal environmental/economic dispatch methodology for electric power generation is developed in this paper. Aside from the conventional economic dispatch, constraints on air quality (such as those specified by the U.S. Environmental Protection Agency) are added to the minimum fuel cost problem. Using the integrated Gaussian puff model based on the statistical turbulent theory, rapid dynamic features of pollutant dispersion and its forecast surrounding the plants are emphasized. By applying a convex programming algorithm repeatedly, a set of marginal environmental imposts for the power plants at different times are obtained. Such imposts are incorporated with the fuel cost in the ordinary short-term economic dispatching program to indirectly account for the environmental impact of power generation on the quality of the ambient air. The approach is specifically taken to have little modification for existing economic dispatch programs and be implemented for real power networks. The proposed approach has been simulated in a power system with three plants and three monitoring points.     

面向高比例新能源电网的重大耗能企业需求响应调度

随着国家“双碳”重大战略的提出,高比例新能源并网将成为我国电力能源转型的重要态势.针对火电机组、配电网和需求侧关联的系列运行约束制约了电网对高比例新能源的有效消纳这一问题,本文提出重大耗能企业这一主要电力负荷参与网需求响应(Demand response, DR)的研究思路,通过重大耗能企业与电网协调调度促进新能源消纳,并获得经济补偿以减少运行成本.研究首先基于混合需求侧响应机制,提出以重大耗能企业、新能源、火电机组为核心的协调调度方法,并根据新能源预测值-预测误差的信息依存顺序提出了两步调度策略.在此基础上,进行生产过程行为建模以实现重大耗能企业需求侧响应决策描述,并建立高比例新能源并网的重大耗能企业需求响应与电网协调调度优化模型.最后,基于烟台电网实际系统进行算例分析,验证了重大耗能企业通过需求响应参与电网协调调度以及两步调度策略的有效性.     

Synergic predator-prey optimization for economic thermal power dispatch problem

This paper introduces a synergic predator-prey optimization (SPPO) algorithm to solve economic load dispatch (ELD) problem for thermal units with practical aspects. The basic PPO model comprises prey and predator as essential components. SPPO uses collaborative decision for movement and direction of prey and maintains diversity in the swarm due to fear factor of predator, which acts as the baffled state of preys’ mind. In the SPPO, the decision making of prey is bifurcated into corroborative and impeded parts. It comprises four behaviors namely inertial, cognitive, collective swarm intelligence, and prey's individual and neighborhood concern of predator. The prey particle memorizes its best and not-best positions as experiences. In this research work, to improve the quality of prey swarm, which influence convergence rate, opposition based initialization is used. To verify robustness of proposed algorithm general benchmark problems and small, medium, and large power generation test power system are simulated. These test systems have non-linear behavior due to multi-fuel options and practical constraints. The constraints of prohibited operating zone and ramp rate limits of power generators’ are handled using heuristics. Newton–Raphson procedure is exploited to attain the transmission losses using load flow analysis. The outcomes of SPPO are compared with the results described in literature and are found satisfactory.     

无线传感器网络动态功率管理方法

为了最大限度节约能源的使用,使无线传感器网络使用寿命延长,提出了利用小波和Kalm an滤波的一种动态功率管理方法。该方法通过使用小波、Kalm an和AR对下一事件到达时间的预测来决定传感器进入何种的功率状态,使得在尽可能低的事件丢失率下减少传感器总体能量损耗。仿真实验结果表明:这种方法是有效的。     

Modified neo-fuzzy neuron-based approach for economic and environmental optimal power dispatch

At the central energy management center in a power system, the real time controls continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is minimized while all the operating constraints are satisfied. However, due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained economic dispatch formulation is to estimate the optimal generation schedule of generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques become very time consuming and computationally extensive for such complex optimization tasks. These methods are hence not suitable for on-line use. Neural networks and fuzzy systems can be trained to generate accurate relations among variables in complex non-linear dynamical environment, as both are model-free estimators. The existing synergy between these two fields has been exploited in this paper for solving the economic and environmental dispatch problem on-line. A multi-output modified neo-fuzzy neuron (NFN), capable of real time training is proposed for economic and environmental power generation allocation.This model is found to achieve accurate results and the training is observed to be faster than other popular neural networks. The proposed method has been tested on medium-sized sample power systems with three and six generating units and found to be suitable for on-line combined environmental economic dispatch (CEED).     

巨型风电并网系统的协同自律控制

巨型风电场并网发电的调度和控制是我国电力系统发展亟需解决的关键难题之一．本文提出了风-水-气协同自律控制的理念和理论,给出了消纳巨型风电的两种基本调度和控制策略,即基于智能调度自动化系统（smart energy manage system,SEMS）的集中式控制和基于协同自律控制的调度策略．本文对比分析了上述两种控制策略的适用情景,指出后者更加合理和高效．进一步,本文探讨了发展基于协同自律控制的风-水-气联合调度系统所需关键技术,试图为解决我国巨型风电并网发电调度难题给出一种方略．     

考虑源荷随机性的跨区互联电网直流联络线调度学习优化

在跨区互联电网中,充分利用直流联络线调度能力可以有效地平衡电力资源的配置,促进新能源的消纳.本文针对源荷不确定性的跨区互联电网直流联络线调度问题,首先用连续马尔科夫过程模型描述互联电网中风电出力与负荷需求随机动态特性;然后在功率平衡及联络线日交易电量约束等实际运行要求前提下,将直流联络线调度优化问题建立成离散马尔科夫决策过程模型.在该模型下,调度机构根据互联电网系统各时段源荷的功率情况,动态调整联络线输电计划和配套的柔性负荷调节方案,以达到提升系统运行效益的优化目标;最后引入强化学习方法对调度策略进行优化求解.通过学习优化,系统平均日运行代价显著下降且最终收敛.实验结果表明考虑源荷随机性的直流联络线动态调整方法可有效地提高互联电网发输电系统的运行效益.