An evaluation of the optimal generation mix for controlling carbon dioxide emissions

This paper presents an efficient computational algorithm for selecting the optimal generation mix considering CO₂ emissions. To demonstrate the effectiveness and feasibility of the proposed method, a fundamental study of the evaluation of the optimal generation mix for controlling CO₂ emissions is indicated. Furthermore, by using a parametric analysis which considers load characteristics as parameters, a general trend for the optimal generation mix which is affected by controlling CO₂ can be derived. The proposed method is based on an optimization method known as simulated annealing. In the method, solutions in a generation mix problem are equivalent to state of a physical system, and the cost of a solution is equivalent to the energy of a state. The proposed method can easily accommodate not only CO₂ emissions but also many practical constraints of generation expansion planning, such as integer solutions of unit capacities, condition of existing units, and so on. Case studies with various annual load patterns (combinations of annual load factors and the shapes of annual load duration curve) are presented and discussed. Consequently, a general trend for selecting generation technologies that should be added to a power system is derived, i.e., a useful guideline for studying generation expansion planning under controlling CO₂ emissions can be provided.     

基于改进遗传算法的风力－柴油联合发电系统扩展规划

提出基于改进遗传算法的风力-柴油联合发电系统容量扩展规划模型,在满足系统规划和运行等非线性约束条件下,寻求总成本最小的容量扩展方案。在规划总成本中除了计及机组建设的投资费用和运行费用以外,还考虑了因电力供给不足所造成的需求侧停电损失成本。在模型中采用蒙特卡罗方法计算系统的概率性发电成本,不仅考虑了风速随机性、机组随机停运、风速序列和负荷序列相关性,而且考虑了风电穿透功率极限的约束。算例表明,文中所提出的模型和算法是可行的,能对风力发电的规划设计提供一定的帮助。     

一种电力市场环境下的电源规划多智能体模型

电源规划一直是电力系统中重要问题之一。在电力市场环境下,这一问题更加复杂、迫切。针对这一问题,基于多智能体技术,提出了电力市场环境下发电集团电源投资规划模型。首先,构建了一个电力市场发电侧的双层优化架构,以实现市场竞价问题与电源规划问题相结合。其次,设计了发电集团、发电市场以及发电厂等智能体,并引入遗传算法、Q-learning算法完善各智能体寻优能力。通过算例验证了所建立的模型是可行的、有效的,可为电站建设决策部门提供有价值的参考。     

基于相关场景聚类的发输电联合扩展规划

电源规划和输电网规划是相互影响的,为应对电源和输电网联合规划,建立了多场景发输电联合扩展规划模型。该模型把支路分为已有支路和待选支路,并分别建立直流潮流方程以使得发输电联合扩展规划成为混合整数线性规划问题,其求解可使用成熟的混合整数线性规划软件。发输电联合扩展规划中负荷和风电的场景确定需要考虑相关性和空间分布特征,提出了保持相关性的负荷风电场景聚类方法。最后,通过在MATLAB平台上编写计算程序,使用标准Garver-6节点和IEEE RTS-24节点系统算例验证了所提方法的有效性,并通过对比说明了负荷空间分布对发输电联合扩展规划的影响。     

SA-PSO在水火电混合电力系统电源规划中的应用

电源规划是电力系统电源布局的战略决策,在电力系统规划中处于十分重要的地位。其核心问题是要确定在规划期内随着负荷的增长,系统应在何时、何地、建什么类型、多大容量的电厂。由于其本身的的复杂性,用传统的优化方法求解需采取简化措施,寻求一个满足各种约束条件和可靠性指标及环保要求的最优电源建设方案,以满足系统负荷发展的需要。为此,提出一种粒子群算法与模拟退火算法结合的模拟退火粒子群算法,并将其用于求解复杂的、非线性的水火电混合电力系统(包含核电)电源规划问题。该组合算法在粒子群算法中引入了模拟退火算法成功的提高了基本粒子群算法的全局搜索能力。算例结果表明:该算法能可靠、快速的收敛到全局最优解,特别适合于大型电力系统的中长期电源规划。     

碳排放及燃煤约束下的电源规划及其效益评价

优化电源规划是应对电力系统"碳锁定"效应,促进电力系统减少碳排放的重要举措。考虑到国内以煤炭为主的能源结构,有必要分析碳排放及燃煤约束对电源规划的影响。在分析碳捕集电厂运行特性的基础上,建立了考虑碳排放及燃煤约束的低碳电源规划模型,并在模型中采用典型日的运行校验系统是否具备足够的调峰能力。基于国内典型地区电力系统,对不同碳排放总量约束、不同可再生能源发展场景及不同煤炭资源供应约束下的常规火电及碳捕集电厂的扩展规划进行了优化分析,分析结论表明碳捕集电厂可以充分适应电力系统的碳减排目标,满足新能源大规模发展的调峰需求。     

An efficient algorithm for optimal generation mix taking into account fuzziness of decision making and planning parameters

This paper presents an efficient computational algorithm for selecting the optimal generation mix under uncertain circumstances. Subjective, experiential or linguistic uncertainties are selected from among various uncertainties, i.e., we treat fuzziness in generation expansion planning. The fuzziness can be divided into: (1) the fuzziness of decision making; and (2) the fuzziness of some planning parameters, such as load growth, fuel price, and so on. Both classes of fuzziness are integrated into a fuzzy decision based on fuzzy sets theory, and then the optimal generation mix can be determined by the Fuzzy Dynamic Programming (FDP) technique. The proposed method, which is based on the dynamic programming technique, is extended by using the Bellman-Zadeh maximizing decision. In the method, each generation technology and generation capacity are selected as a stage and state, respectively. The proposed method can easily accommodate not only the fuzziness but also many constraints of generation expansion planning, such as integer solutions of unit capacities, condition of existing units, and so on. Furthermore, the arbitrary shape of membership function can be used. The effectiveness and feasibility of the proposed method are demonstrated on a typical power system model.     

改进免疫算法在电力系统电源规划中的应用

目前,遗传算法作为一种基于人工智能技术的优化算法在电力系统的电源规划中已经得到广泛应用,然而其遗传操作繁杂、计算量大,易产生退化、早熟等问题使其应用受到局限.文章提出一种基于自然分段式编码的改进免疫算法(Modified Immune Algorithm,MIA),使用该方法可以大大减少电源规划的计算量,提高计算效率,并可充分利用特征信息灵活求解.该算法具有全局多峰搜索能力以及优异的收敛特性,同时可避免退化、早熟等问题的发生.算例结果表明,该算法可以较好地求解电源规划问题,并且具有广阔的发展空间.     

改进的遗传算法在中期发电规划中的应用

给出了发电扩展规划的基本数学模型。为了满足日益严峻的电网调峰问题,在发电扩展规划模型中引入了峰谷差约束,然后讨论了应用于发电扩展规划的简单遗传算法的交叉和变异因子固定不变和有时进化缓慢的缺点。提出了一种改进的遗传算法。该算法能根据父代群体的进化质量动态地调整交叉和遗传因子,使进化群体始终探测新的超平面并获得新的最优个体,能克服过早收敛,也不易陷入局部最优解,从而有效地改善了算法收敛速度和最好解。最后给出了上海市2000－2007年的中期发电扩展规划的实际算例,分别用简单遗传算法和改进遗传算法进行了计算。计算结果表明,改进的遗传算法由于能自动调整遗传因子,在进化过程中能激励所有个体协作共同度过进化停滞期而能获得较优解。     

遗传禁忌混合算法及其在电网规划中的应用

电网规划是一个较难解决的NP难问题。文中首先就遗传算法、禁忌搜索算法(TS)及其两者的混合算法在旅行商问题(TSP)中的应用来比较它们之间的优缺点,认为采用了TS变异算子的改进遗传算法将大大提高其优化能力;然后通过该混合算法在典型电网扩展规划算例中的应用来看,认为该混合算法适用于求解复杂的电网规划问题;最后通过对该混合算法在求解实际的城市中压配电网络规划问题时与其他两种单一算法的结果比较来看,其搜索效率相比单一算法得到了很大程度的提高,体现了很好的应用前景。     

The application of artificial intelligent tools to the transmission expansion problem

The purpose of transmission expansion problem (TEP) is to determine the timing and type of new transmission facilities. The TEP has been formulated as an optimization problem. The objective was to minimize the transmission investment costs that handle the increased load and the additional generation requirements in terms of line additions and power losses. Several constraints were considered including the power flow on the network lines, the right-of-way's validity and its maximum line addition. The TEP was then solved using artificial intelligence (AI) tools such as the genetic algorithm, Tabu search and artificial neural networks (ANNs) with linear and quadratic programming models. The effectiveness of the AI methods in dealing with small and large-scale systems was tested through the applications of a six-bus system, the IEEE-24 bus network and a Saudi Arabian network. The hybridization of GA, TS and ANN has several features. Its results confirm that it is superior in dealing with a large-scale problem in which the size of the search spaces increases exponentially with the dimension of the network.     

A multi-year transmission planning under a deregulated market

Due to the deregulation of power industry, the transmission expansion plan is different from the process done by the integrated monopolies. In a monopoly electric market, the transmission expansion plan is carried out by the vertically integrated utility. The power company integrates its generations’ exploiting plans and its transmission expansion plans to maintain the system reliability. While in a deregulated power industry, generation, transmission, and distribution companies belong to different owners. The problem becomes more difficult. Generators experiencing transmission constraints can be expected to lobby for new transmission facilities that might relieve their constraints, while generators closer the load centers will likely toward not to build any new transmission facilities that would increase their competition. In order to provide a fair environment for all market participants, this paper proposed a reasonable expansion plan taking the operation cost, load curtailment cost, and investment cost into account. Due to the complexity of this model, the algorithm that combines the genetic algorithm with the linear programming method (GA-LP) is used to solve this problem. The 6-bus system and 24-bus IEEE reliability test system are used to verify the proposed model, and comparisons of test results between the proposed model and the traditional model are also demonstrated in this paper.     

Planning for distributed generation

Distributed generation (DG) can be incorporated into distribution planning as an option along with traditional feeder and substation options. In place of rigid capacity planning rules, the planning process needs to incorporate more detailed simulations of capacity constraints and customer damage costs because utilities now assume more risk. Planning tools need to simulate load variations, contingencies, dispatch and control action to more accurately determine the capacity limits and related costs. This is especially important when considering DG because the siting and dispatch have an important impact on the benefits to the utility. Equally important is the evaluation of economic risk given uncertainties such as load growth. Because of the added complexities associated with these analysis techniques, the planning process must incorporate appropriate screening tools to determine the depth of analysis needed for particular projects, thereby making more efficient use of already scarce planning resources     

Transmission Congestion and Competition on Power Generation Expansion

Generation expansion planning involves decisions on location and capacity of new generation, which may lead to adding or relieving congestion in transmission lines and to increasing or reducing competition in deregulated markets. Generation expansion may encounter congestion in the transmission network by constrained single-line flows as well as flowgate transfer capabilities. In this paper, a model to study the interaction between competition and transmission congestion on power generation expansion is proposed. The generation expansion problem is modeled as a Cournot competition game. Network transmission constraints are included in the optimal generation expansion problem to comply with power flow limits. Results from a five-bus power network and the IEEE 24-bus system are presented and discussed     

基于免疫遗传算法的含分布式电源配网规划

大量分布式电源的接入使配电网规划更加复杂.在限制分布式电源接入总量和考虑多种约束的基础上,建立以配网年费用最小为目标函数的规划模型.针对该模型的特点,采用一种新型免疫遗传算法( IGA)对配电网扩展规划进行优化.该算法综合了免疫系统和遗传算法的优点,可实现群体收敛性和个体多样性间的动态平衡,具有良好的全局收敛能力.分别...     

Operational planning for pumped storage power station using heuristic techniques

A computer algorithm is described that coordinates, economically, the operation of a large pumped storage scheme with an existing thermal generation system. It uses heuristic techniques to establish a system model for operation, based largely on the manual merit order method, and in incorporates several operating constraints, including generator maximum and minimum generation levels, minimum shutdown times and loading and unloading rates. Designed specifically as an operational planning tool, the algorithm produces a unit commitment and load schedule for every half-hour interval of any load curve supplied. By costing this schedule with and without the pumped storage scheme, a pumping cost is obtained that can then be compared with other methods of frequency control. Finally, by extending the schedule to include hydrogeneration, the economic benefit of using the scheme in this mode may also be evaluated.     

基于禁忌搜索算法的微电网源/荷安全经济调度

合理求解微电源/荷协调调度方案是微电网安全经济运行的理论依据。在分析各分布式电源发电成本和可控负荷特性的基础上,建立两种时域运营成本最低的目标函数。以微电网有功功率平衡、分布式电源出力上下限和爬坡速率以及可控负荷的用电特性为约束,建立微电网源/荷协调安全经济调度数学模型。以优先利用可再生能源和调度成本最低为原则制定优化调度策略。利用禁忌搜索算法求解微电网源/荷调度模型得到微电网源/荷调度方案,并与应用单纯形法的全局最优解结果进行比较。算例分析表明禁忌搜索算法解得的调度方案接近于全局最优解,以日时域24 h为调度周期的调度方案与以单位时域1 h为调度周期的调度方案结果相近。     

Short-term thermal generation scheduling using improved immune algorithm

This paper proposes a new immune algorithm (NIA), which merges the fuzzy system (FS), the annealing immune (AI) method and the immune algorithm (IA) together, to resolve short-term thermal generation unit commitment (UC) problems. This proposed method differs from its counterparts in three main aspects, namely: (1) changing the crossover and mutation ratios from a fixed value to a variable value determined by the fuzzy system method, (2) using the memory cell and (3) adding the annealing immune operator. With these modifications, we can attain three major advantages with the NIA, i.e. (1) the NIA will not fall into a local optimal solution trap; (2) the NIA can quickly and correctly find a full set of global optimal solutions and (3) the NIA can achieve the most economic solution for unit commitment with ease. The UC determines the start-up and shut-down schedules for related generation units to meet the forecasted demand at a minimum cost while satisfying other constraints, such as each unit's generating limit. The NIA is applied to six cases with various numbers of thermal generation units over a 24-h period. The schedule generated by the NIA is compared with that by several other methods, including the dynamic programming (DP), the Lagrangian relaxation (LR), the standard genetic algorithm (GA), the traditional simulated annealing (SA) and the traditional Tabu search (TS). The comparisons verify the validity and superiority in accuracy for the proposed method.     

Flexible generation mix under multi objectives and uncertainties

Criteria for evaluating flexibility of a generation mix are presented, and an efficient computational algorithm for determining a flexible generation mix considering uncertainties in the power system expansion planning is developed. The flexible generation mix problem is formulated as a multiobjective optimization problem and solved by the dynamic programming technique in which type of generation plant is selected as a stage and generation capacity is selected as a state. The proposed method can treat easily not only the uncertainties but also many constraints of generation planning such as integer solutions of generation capacities, reliability constraints, and so on. The effectiveness and feasibility of the proposed method are demonstrated on a typical power system model     

Value-based power system reliability planning

In a competitive energy market, in which power supply reliability can influence customer purchasing decisions, electric utilities throughout the world are rapidly recognizing that they cannot ignore customer preferences. Today's energy market is characterized by intense price competition, and electric utilities are faced with new challenges of large debts, budget constraints, safety, environment and economic issues, lower load growth than in the past, need for more involvement of public at large in the planning and design process, and more competitive nonconventional suppliers of electricity. In addition, in a deregulated competitive energy market, electric utilities are under conflicting pressures of providing even higher standards of service reliability and holding the line on rates. Value-based system facility planning offers a rational response to these conflicting customer demands. This paper presents two case studies to illustrate the basic concepts and applications of value-based power system planning. The paper also presents a methodology for determining the unique composite customer cost of interruptions (i.e. damage functions) for changing area system network configurations. These techniques are presently being used in the decision-making processes at Alberta Power Limited, Edmonton, Alta., Canada