Dynamic price forecast in a competitive electricity market

Price forecast is a key issue in competitive electricity markets. It provides useful information for the market players and the regulators, in both short and long run. Different approaches have been proposed and implemented. A new dynamic approach for forecasting the market price of electricity in the short term is proposed. The price dates are first clustered according to different types of daily profiles and then, given a proper function representing the trend in price, the set of unknown parameters are identified based on the zeroing of a Lyapunov function. The forecast can be dynamically updated with the latest data available. Higher weight can be attributed to this data in determining the future prices. The proposed approach is validated with reference to real systems in the form of the Italian, New England and New York electricity markets. In addition, an extensive price forecast is provided for the Italian market, an example of a young market that is rather difficult to predict patterns for.     

基于遗传算法和灰色神经网络的电力机车产品需求预测方法

目的 应对快速多变的市场,提前预知市场发展,制定相应的排产计划,使企业在竞争中占据先发优势。方法 目前基于灰色神经网络的预测算法,准确地预测产品需求通常需要连续且大量的样本数据,对小数据非线性系统的预测结果精确度低、可靠性差,针对这一问题,提出一种耦合遗传算法的灰色神经网络预测方法,综合灰色模型和神经网络理论,构建了面向产品订单量需求预测的灰色神经网络模型;通过电力机车产品实例分析了模型的预测性能;为解决预测过程中模型早熟收敛的问题,利用遗传算法对训练网络的权重和阈值进行了迭代优化。结论 研究结果表明,优化后产品预测模型的精确性和鲁棒性得到提高,验证了所设计方法的可行性。     

Social welfare analysis of the iberian electricity market accounting for carbon emission prices

In this study, the authors analyse the social welfare impact of the integration of Portugal and Spain in the Iberian electricity market (MIBEL), taking into account the CO₂ price for emissions trading. They model the impact of emissions trading on the daily clearing prices and generation scheduling, and its effects on the benefits of integration as a whole. They compare the impact of market integration in Portugal and Spain and show that the welfare impact of the MIBEL is dependent on the CO₂ prices. From their analysis, they conclude high CO₂ prices lead to a change in the merit order. Moreover, natural gas is the generation technology that most benefits from transmission constraints and from high CO₂ prices, as in the base case it is mainly used as a peak technology. The authors have also found that increases in the CO₂ prices do not lead to higher profits. Overall, the introduction of the MIBEL will increase social welfare by reducing generation costs and prices.     

Multi-product selective newsvendor problem with service level constraints and market selection flexibility

We consider a multi-product multi-market newsvendor problem where the decision-maker could select some markets to serve. The considered problem involves the integration of procurement and market selection decisions. The products are procured from an external supplier. We assume that the realised demand for each product should be satisfied. In the case of shortages, the firm procures items at a higher cost. The paper considers the case where the selling prices, market entry costs, and product demand distributions are market dependent. Specifically, we discuss three cases of the Multi-Product Selective Newsvendor Problem: flexible market entry case, full market entry case and partial market entry case. The mathematical models of the above cases result in binary nonlinear programmes. We develop solution algorithms for solving the resulting combinatorial problems. Some managerial insights are provided.     

平准化生产模型研究及应用分析

按照平准化生产原理,探求了汽车平准化生产的多品种均衡性和排程复杂性。在此基础上,构建了基于时段特征的混流切换模型,并进一步研究了混流切换模型下的多品种混流排程方法,解析了生产中获得排程满意解的基本过程,通过实例验证了混流切换模型和生产比倒数法的有效性,降低了实际生产排程复杂性。     

One step-ahead ANFIS time series model for forecasting electricity loads

In electric industry, electricity loads forecasting has become more and more important, because demand quantity is a major determinant in electricity supply strategy. Furthermore, accurate regional loads forecasting is one of principal factors for electric industry to improve the management performance. Recently, time series analysis and statistical methods have been developed for electricity loads forecasting. However, there are two drawbacks in the past forecasting models: (1) conventional statistical methods, such as regression models are unable to deal with the nonlinear relationships well, because of electricity loads are known to be nonlinear; and (2) the rules generated from conventional statistical methods (i.e., ARIMA), and artificial intelligence technologies (i.e., support vector machines (SVM) and artificial neural networks (ANN)) are not easily comprehensive for policy-maker. Based on these reasons above, this paper proposes a new model, which incorporates one step-ahead concept into adaptive-network-based fuzzy inference system (ANFIS) to build a fusion ANFIS model and enhances forecasting for electricity loads by adaptive forecasting equation. The fuzzy if-then rules produced from fusion ANFIS model, which can be understood for human recognition, and the adaptive network in fusion ANFIS model can deal with the nonlinear relationships. This study optimizes the proposed model by adaptive network and adaptive forecasting equation to improve electricity loads forecasting accuracy. To evaluate forecasting performances, six different models are used as comparison models. The experimental results indicate that the proposed model is superior to the listing models in terms of mean absolute percentage errors (MAPE).     

Perfect competition vs. strategic behaviour models to derive electricity prices and the influence of renewables on market power

A variety of fundamental modelling approaches exist using different competition concepts with and without strategic behaviour to derive electricity prices. To investigate the quality and practicability of these different approaches in energy economics, a perfect competition model, a Cournot model and a Bilevel model are introduced and applied to different situations in the German electricity market. The three electricity market approaches are analysed with respect to their ability to represent electricity prices and the possibility of market power abuse. Market prices are taken as a benchmark for model validity. As a result, the perfect competition model fits best to today’s market situation in most hours of the year. The Bilevel approach explains prices in high load hours sometimes better than the competition model. But complexity and calculation time increase disproportionately. In addition to the analysis of model quality, we use three scenarios to quantify how a high renewable feed-in influences the ability to abuse market power. Results show that the ability to address market power strongly depends on the amount of installed capacities.     

Emission allowances auction for an oligopolistic electricity market operating under cap-and-trade

The authors consider a yearly auction where electricity generating companies (Gencos) bid to receive yearly green house gas (GHG) emission allowances. Gencos sell electricity in an oligopolistic electricity market that clears on an hourly basis and operates under a cap-and-trade emissions regulation scheme. Gencos strategically self-allocate their yearly allowance into hourly allowances that they then use to take part in the hourly electricity market. If a Genco emits above or below its self-allocated allowance for that hour then, in the first case, the hourly deficit is made up by buying an allowance from an external market, whereas in the second the hourly allowance surplus is sold to the external market. Recognising that the levels of power and emissions produced by the Gencos as well as the associated prices throughout the year will be influenced by both the yearly and hourly allowances, the auction maximises an objective function that is equal not only to the total amount bid by the Gencos to obtain allowances but also includes the yearly social welfare. This study proposes an approach that considers all of the above-mentioned points in a coordinated fashion and can be viewed as a mathematical program (the allowance auction) subject to a Nash equilibrium problem (the distribution by each Genco of its yearly allowance into hourly allowances), which in turn is subject to the Cournot?Nash equilibrium conditions of the hourly oligopolistic electricity market.     

Optimal distributed generation location using mixed integer non-linear programming in hybrid electricity markets

This study presents mixed integer non-linear programming (MINLP) approach for determining optimal location and number of distributed generators in hybrid electricity market. For optimal location of distributed generation (DG), first the most appropriate zone has been identified based on real power nodal price and real power loss sensitivity index as an economic and operational criterion. After identifying the suitable zone, mixed integer non-linear programming approach has been applied to locate optimal place and number of distributed generators in the obtained zone. The non-linear optimisation approach consists of minimisation of total fuel cost of conventional and DG sources as well as minimisation of line losses in the network. The pattern of nodal real and reactive power prices, line loss reduction and fuel cost saving has been obtained. The results have also been obtained for pool electricity market model for comparison. The impact of demand variation on the results has also been obtained for both the market models. The proposed MINLP-based optimisation approach has been applied for IEEE 24 bus reliability test system.     

The impact of consumer price forecasting behaviour on the bullwhip effect

As prices fluctuate over time, a strategic consumer may buy more in advance to reduce his or her future needs in anticipation of higher prices in the future, or may choose to postpone a purchase in anticipation of lower prices in the future. We investigate the bullwhip effect from a consumer price forecasting behavioural perspective in the context of a simple two-level supply chain composed of a supplier and a retailer. We consider two different forms for the demand function – linear and iso-elastic demand functions, both depending on the prices in multiple periods. Assuming that the retailer employs an order-up-to inventory policy with exponential smoothing forecasting technology, we derive analytical expressions for the bullwhip effect under the two demand functions, and extend the results to the multiple-retailer case. We find that consumer forecasting behaviour can reduce the bullwhip effect, most significantly when the consumer sensitivity to price changes is medium (approximately 0.5) for both the demand forms. In addition, for iso-elastic demand, the mitigation of the bullwhip effect induced by consumer price forecasting behaviour becomes more significant as the product price sensitivity coefficient and standard deviation of the price decrease. These findings are applicable to the development of managerial strategies by supply chain members that are conducive to bullwhip effect reduction through customer behaviour.     

Methods for customer and demand response policies selection in new electricity markets

Different methodologies are available for clustering and classification purposes. The objective of the research is to prove the capability of self-organising maps (SOMs) to classify customers and their response potential from distributor, commercialiser, or customer electrical demand databases, with the help of load response modelling methodologies as support tools. The search for customer suitability is restricted to day-ahead and real-time products, in which interest is growing in developed countries. Therefore customer demand and response (demand and distributed generation policies) have been tested and compared with price curves. Both steps have been performed through SOMs. The results clearly show the capability of this approach to improve data management and easily to find coherent policies to accomplish cleared-demand offers in different prices scenarios.     

Production scheduling with outsourcing scenarios: a mixed integer programming and efficient solution procedure

When market demand exceeds the company's capacity to manufacture, outsourcing is commonly considered as an effective alternative option. In traditional scheduling problems, processing of received orders is just possible via in-house resources, while in practice, outsourcing is frequently found in various manufacturing industries, especially in electronics, motor and printing companies. This paper deals with the scheduling problem, minimising the cost of outsourcing and a scheduling measure represented by weighted mean flow time, in which outsourcing of manufacturing operations is allowed through subcontracts. Each order can be either scheduled for in-house production or outsourced to an outside supplier in order to meet customer due dates. In this problem, not only should the sequence of orders be determined, but also decision on picking the jobs for outsourcing, selecting the appropriate subcontractor, and scheduling of the outsourced orders are considered as new variables. To formulate the given problem, four different outsourcing scenarios are derived and mixed integer programming models are developed for each one separately. Furthermore, to solve the suggested problem, a computationally effective team process algorithm is devised and then a constraint handling technique is embedded into the main algorithm in order to ensure satisfaction of customer due dates. Numerical results show that the suggested approach possesses high global solution rates as well as fast convergence.     

Mixed-model assembly line scheduling using the Lagrangian relaxation technique

The increasing market demand for product variety forces manufacturers to design mixed-model assembly lines on which different product models can be switched back and forth and mixed together with little changeover costs. This paper describes the design and implementation of an optimization-based scheduling algorithm for mixed-model compressor assembly lines at Toshiba with complicated component supply requirements. A separable integer optimization formulation is obtained by treating compressor lots going through a properly balanced line as undergoing a single operation, and the scheduling goal is to delivery products just in time while avoiding possible component shortage. The problem is solved b y using Lagrangian Relaxation (LR). Several generic defects of LR leading to slow algorithm convergence are identified based on geometrical insights, and are overcome by perturbing/ changing problem parameters. Numerical testing shows that near-optimal schedules are efficiently obtained, convergence is significantly improved, and the method is effective for practical problems. The system is currently under deployment at Toshiba     

Combined particle swarm optimization and heuristic fuzzy inference systems for a smart home one-step-ahead load forecasting

This article presents a hybrid approach that combines particle swarm optimization (PSO) and heuristic fuzzy inference system (HFIS) for smart home one-step-ahead load forecasting. Smart home load forecasting is an important issue in the development of smart grids. Generally, the electricity consumption of a household is inherently nonlinear and dynamic and heavily dependent on the habitual nature of power demand, activities of daily living and on holidays or weekends, so it is often difficult to construct an adequate forecasting model for this type of load. To address this problem, a hybrid model, consisting of two phases, is proposed in this article. In the first phase, the popular PSO algorithm is used to determine the locations of fuzzy membership functions. Then, the proposed HFIS technique is used to develop the one-step-ahead load forecasting model in the second phase. Because of the robust nature of the proposed HFIS technique, which does not need to retrain or re-estimate model parameters, it is very suitable for smart home load forecasting. The proposed method was verified using two different households’ load data. Simulation results indicate that the proposed method produces better forecasting accuracy than existing methods.     

Optimization of Short Load Forecasting in Electricity Market of Iran Using Artificial Neural Networks

Accurate short-term load forecasting (STLF) is one of the essential requirements for power systems. In this paper, two different seasonal artificial neural networks (ANNs) are designed and compared in terms of model complexity, robustness, and forecasting accuracy. Furthermore, the performance of ANN partitioning is evaluated. The first model is a daily forecasting model which is used for forecasting hourly load of the next day. The second model is composed of 24 sub-networks which are used for forecasting hourly load of the next day. In fact, the second model is partitioning of the first model. Time, temperature, and historical loads are taken as inputs for ANN models. The neural network models are based on feed-forward back propagation which are trained and tested using data from electricity market of Iran during 2003 to 2005. Results show a good correlation between actual data and ANN outcomes. Moreover, it is shown that the first designed model consisting of single ANN is more appropriate than the second model consisting of 24 distinct ANNs. Finally ANN results are compared to conventional regression models. It is observed that in most cases ANN models are superior to regression models in terms of mean absolute percentage error (MAPE).     

Active demand management for substitute products through price optimisation

This paper presents a systematic mathematical programming approach for active demand management in process industries. The proposed methodology aims to determine optimal pricing policies as well as output levels for substitute products, while taking into consideration manufacturing costs, resource availability, customer demand elasticity, outsourcing and market competition. First, profit maximisation analytical formulae are derived for determining Nash equilibrium in prices for a duopolistic market environment where each company produces only one product. An iterative algorithm is then proposed so as to determine the decision-making process by solving a series of non-linear mathematical programming (NLP) models before determining the Nash equilibrium in prices for the competing companies. The proposed algorithm is extended in order to accommodate the case of multi-product companies, each one selling a set of substitute products at different prices. The applicability of the proposed methodology is demonstrated by a number of illustrative examples.     

Coordination of dual-channel supply chains under demand disruptions management decisions

This paper examines optimal decisions and coordination models for a dual-channel supply chain when the two end competition market demands are simultaneously disrupted. Firstly, we developed the pricing and production decisions models without demand disruptions and propose a revenue sharing contract to coordinate the dual-channel supply chain where the manufacturer is a Stackelberg leader and the retailer is a follower. We derived the conditions under which the maximum profit can be achieved in detailed. We compared the profits under normal case and disrupted case and quantified the information value of knowing demand disruptions. We proposed an improved revenue sharing contract to coordinate the dual-channel supply chain with demand disruptions. The results indicate that the adjusting prices and production quantity are the optimal decisions whether the demand disruptions case or normal case. We also find that the original revenue sharing contract is a special case of improved revenue sharing contract and the market scale change, channel substitutability and deviation cost affected the improved revenue sharing contract under demand disruptions. Finally, we further conduct numerical experiments to show how the demand disruption affects the decisions.     

Swarm intelligence-based strategic bidding in competitive electricity markets

In the competitive electricity markets, formation of supply bid is one of the main concerns, where suppliers have to maximise their profit under incomplete information of other competing generators. An environment is described in which suppliers bid strategically to sell electricity in a pool market. The bidding decision is optimised from a single supplier's viewpoint in both block-bid and linear-bid models of an electricity market. To include uncertain behaviour of other competing suppliers, two different probabilistic models are used. Their bids are constructed using probability distribution functions obtained from the decision-maker's observations of historical market data. Single supplier's decision-making problem is solved by a modern population-based heuristic algorithm, known as particle swarm optimisation (PSO). Search procedure of PSO is based on the concept of combined effect of cognitive and social learning of the members in a group. The effectiveness of the proposed method is tested with examples and the results are compared with the solutions obtained using the genetic algorithm approach.     

An innovative air-conditioning load forecasting model based on RBF neural network and combined residual error correction

Accurate air-conditioning load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. They have developed many forecasting methods, such as multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), grey model (GM) and artificial neural network (ANN), in the field of air-conditioning load prediction. However, none of them has enough accuracy to satisfy the practical demand. On the basis of these models existed, a novel forecasting method, called ‘RBF neural network (RBFNN) with combined residual error correction’, is developed in this paper. The new model adopts the advanced algorithm of neural network based on radial basis functions for the air-conditioning load forecasting, and uses the combined forecasting model, which is the combination of MLR, ARIMA and GM, to estimate the residual errors and correct the ultimate foresting results. A study case indicates that RBFNN with combined residual error correction has a much better forecasting accuracy than RBFNN itself and RBFNN with single-model correction.     

企业需求预测准确性存在的问题及其应对策略(二)

鉴于需求预测在企业经营活动中具有重要地位,且会受到受各种因素影响,本文在对企业实际需求预测的方法、过程、系统、管理等问题进行梳理和分析的基础上,指出了通过优化需求预测方法、完善需求预测系统、改进需求预测管理,可有效控制需求预测和未来市场情况的偏差,从而持续提高需求预测的准确性,促进企业生产、销售的良性运行。