The quest for carbon-neutral industrial operations: renewable power purchase versus distributed generation

Integrating renewable energy into the manufacturing facility is the ultimate key to realising carbon-neutral operations. Although many firms have taken various initiatives to reduce the carbon footprint of their facilities, there are few quantitative studies focused on cost analysis and supply reliability of integrating intermittent wind and solar power. This paper aims to fill this gap by addressing the following question: shall we adopt power purchase agreement (PPA) or onsite renewable generation to realise the eco-economic benefits? We tackle this complex decision-making problem by considering two regulatory options: government carbon incentives and utility pricing policy. A stochastic programming model is formulated to search for the optimal mix of onsite and offsite renewable power supply. The model is tested extensively in different regions under various climatic conditions. Three findings are obtained. First, in a long term onsite generation and PPA can avoid the price volatility in the spot or wholesale electricity market. Second, at locations where the wind speed is below 6 m/s, PPA at $70/MWh is preferred over onsite wind generation. Third, compared to PPA and wind generation, solar generation is not economically competitive unless the capacity cost is down below $1.5 M/MW.     

Implementing factory demand response via onsite renewable energy: a design-of-experiment approach

This paper proposes the implementation of demand response (DR) programmes in large manufacturing facilities featuring distributed wind and solar energy. Manufacturing facilities are high consumers of electric power. For this reason, these facilities usually pay exorbitant utility bills, which could be as much as $10–20 million per year. A high consumption of electricity also means that upstream fossil-fuelled power plants must release thousands of metric tonnes of carbon annually during the generation of electricity. DR contracts offer a lower utility rate in return for a load reduction during contingent events (i.e. peak hours). This paper covers the modelling and implementation of an interruptible/curtailable DR programme participated by a manufacturer that possesses onsite renewable generation units. These complementary energy resources allow the manufacturer to meet the curtailment requirements without causing any major electricity shortage that adversely affects the normal production schedule. We developed a stochastic programming model to determine the capacity of the wind turbine and solar panels that maximise the DR programme savings. The optimal solutions are derived based on central composite design methodology.     

Planning of capacity,production and inventory decisions in a generic reverse supply chain under uncertain demand and returns

There is a growing interest for the design and operation of reverse supply chain systems due to the cost and the legislation issues. In this paper, we address the disassembly, refurbishing and production operations in a reverse supply chain setting for modular products such as computers and mobile phones considering the uncertainties in this system, which are the return amounts of the used products and demand for final products. We develop a large-scale mixed integer programming model in order to capture all characteristics of this system, and use two-stage stochastic optimisation and robust optimisation approaches to analyse the system behaviour. In the first stage, we focus on the strategic decisions about the capacities at disassembly and refurbishing sites considering different scenarios regarding the uncertainties in the system. In the second stage, we analyse the operational decisions such as production, inventory and disposal rates. We observe through our extensive numerical analysis that the randomness of demand and return values effect the performance of the system substantially and the uncertainty of the return amounts of used products is much more important than the uncertainty of demand in this system.     

An Empirical Study on China’s Energy Supply-and-Demand Model Considering Carbon Emission Peak Constraints in 2030

China’s energy supply-and-demand model and two related carbon emission scenarios, including a planned peak scenario and an advanced peak scenario, are designed taking into consideration China’s economic development, technological progress, policies, resources, environmental capacity, and other factors. The analysis of the defined scenarios provides the following conclusions: Primary energy and power demand will continue to grow leading up to 2030, and the growth rate of power demand will be much higher than that of primary energy demand. Moreover, low carbonization will be a basic feature of energy supply-and-demand structural changes, and non-fossil energy will replace oil as the second largest energy source. Finally, energy-related carbon emissions could peak in 2025 through the application of more efficient energy consumption patterns and more low-carbon energy supply modes. The push toward decarbonization of the power industry is essential for reducing the peak value of carbon emissions.     

Scheduling a storage-augmented discrete production facility under incentive-based demand response

Demand response (DR) is considered as one of the most important measures for balancing energy supply and demand in the smart grid paradigm. Incentive-based programs, one manifestation of DR, contribute to short-term system stability and prevent critical periods when system stability is at risk by enabling the system operator (SO) to directly change total energy demand. The fact that a third party would be empowered to interfere with internal operations is, however, also one of the major drawbacks of DR that prevents especially industrial consumers from participating with full capacity in such programs. This paper considers an alternative Incentive-based program with application to a discrete manufacturing facility where load reduction curves (LRCs) are generated a priori outlining the potential load reduction in the DR period. The SO uses the LRC to determine the desired level of load reduction for critical periods. To illustrate the generation of the LRC, this paper builds on a flexible flow shop (FFS) formulation for a discrete manufacturing facility and presents a model that includes multiple machine modes and product- and machine-specific energy consumption trajectories. Based on the FFS, a procedure is developed to generate the LRC. The paper also investigates the potential of including a battery energy storage system (BESS) into the production facility and illustrates the effects of the BESS on the LRC.     

风、光、油互补控制器的设计

本文设计开发的一款基于风、光、油互补的充电控制器,可以实现风能和光能转化为蓄电池电能,在无风、无光照的条件下,且蓄电池的能量不足,可以启动油机进行发电,并对蓄电池进行充电。从而实现对可再生能源的充分利用,在保障设备供电的前提下,最大限度的节省燃油。     

风光水多能源电力系统互补智能优化运行策略

高比例可再生能源接入电网,采用风光水互补发电可以提供平滑和稳定的电力供应。针对风光水多能源电力系统,采用大数据和人工智能技术,提出基于随机规划的短期优化运行方法。首先,基于变分自编码器（variational autoencoder,VAE）提出了可再生能源场景生成方法,能够生成符合可再生能源出力特性的多样化场景,并精确刻画可再生能源出力的相关性。其次,基于场景法建立了风光水互补短期优化运行模型,并采用分段线性化的方法将多种非线性约束转化为线性约束,可以保证模型精度的同时实现快速求解。最后,通过对雅砻江下游风光水发电基地的算例仿真,验证了所提的智能优化运行策略的有效性。     

Two-stage simulation optimization for agile manufacturing capacity planning

Capacity planning involves the selection of manufacturing technologies and the allocation of budget to specific equipment acquisitions. In today's highly volatile manufacturing world, an agile capacity-planning tool is required. This tool must provide the mechanism for a company to thrive in an environment of uncertainty. Uncertain future demands make capacity planning and technology selection difficult tasks, whether they are caused by variations in forecasts of direct demand or by upstream variability in a supply chain. In this paper, a practical modelling technique for minimizing the required investment in capacity planning for discrete manufacturing sites under an uncertain demand stream is presented. The method consists of a two-stage stochastic integer program. The first stage characterizes the optimal response of the system under uncertainty. The second stage selects a tool set based on the characterization from the first stage, with the addition of budget constraints. The model is scalable, allowing for multiple products, multiple operations, multiple flow paths including re-entrant flow, and multiple tool types. A simple example is introduced to explain the methodology, followed by the results of a large-scale real-world application in the semiconductor industry.     

计及先进绝热压缩空气储能多能联供特性的微型综合能源系统优化调度模型

先进绝热压缩空气储能（Advanced adiabaticcompressed air energy storage,AA-CAES）是一种清洁的大规模物理储能技术。相对于其他类型的储能技术,AA-CAES技术具有多能流联供的独特特性,这一特性使得其在微型综合能源系统中具有广阔的应用前景。考虑AA-CAES电站的多能联供特性,研究了含AA-CAES电站的微型综合能源系统优化调度策略。介绍了含AA-CAES电站的微型综合能源系统基本构成;基于AA-CAES电站的实际热力学过程,构建AA-CAES电站的冷热电多能流联合调度约束模型;在此基础上,以最小化系统运行成本为目标,建立含AA-CAES电站的微型综合能源系统优化调度模型;最后,采用天津中新生态城的数据进行模型验证。     

Pricing and effort decisions for a supply chain with uncertain information

This paper analyses the pricing and effort decisions of a supply chain with single manufacturer and single retailer. The manufacturer produces a kind of product and then wholesales the product to the retailer, who in turn retails it to customers over a single selling season. The retailer can influence demand through her sales effort. This research depicts the consumer demand, the manufacturing cost and the sales effort cost as uncertain variables. Considering the demand expansion effectiveness of sales effort, one centralised and three decentralised game models are built on the basis of the expected value criterion, and the equilibrium solutions are obtained. We investigate the effects of the parameters’ uncertainty degrees on the pricing and effort decisions. The results indicate that the manufacturer benefits from improvement in demand and cost uncertainties when he has at least bargaining power in the supply chain. The results also imply that the uncertainty degree of sales effort elasticity has an outstanding influence on the pricing and effort decisions, whereas the uncertainty degree of price elasticity has a modest impact on these decisions. We also study the effects of the parameters’ uncertainty degrees on the supply chain from the consumers’ perspective. The results suggest that with a power retailer, the retail price should always be on the high end. Consequently, consumers do not necessarily benefit from a power retailer. When the manufacturer and the retailer have equal bargaining power, consumers do not necessarily benefit from the supply chain, either.     

储能在电网中的应用价值及其商业模式

随着风电、光伏渗透率逐渐增大,其并网给电网稳定运行带来了诸多挑战。储能技术可以有效平抑新能源功率波动,增强新能源发电可控性,提高新能源的并网接入能力,因此在电网中配置储能的相关研究与技术受到越来越多的关注。以储能系统接入电网的功能为切入点,针对储能系统在调频、调峰、备用容量和延缓输配电扩容升级等4种场景中的应用价值进行研究和归纳,讨论了储能相关商业模式的发展前景和未来待研究的关键科学问题。     

A stochastic production planning model under uncertain seasonal demand and market growth

This paper proposes a stochastic production planning model for an international enclosure manufacturing company with seasonal demand and market growth uncertainty. The company purchases material and subassembly from overseas and long lead times have been observed. To prevent excess inventory and stockout, the company is required to forecast its demand and project its purchasing decisions and production load to its key suppliers in an effort to reduce risks for both parties. To assist purchasing and production decisions, a two-stage stochastic production planning model that explicitly includes uncertainty is developed with the goal of minimising the total production, inventory, and overtime costs under all scenarios. The model is solved using real data from the company and results have demonstrated the effectiveness of the model compared with various deterministic models. Parametric analyses are performed to derive managerial insights related to issues such as overtime usage, inventory holding costs and the proper selection of scenarios under pessimist, neutral, and optimist outlooks. The model has been implemented and an annual saving of more than $400,000 in inventory cost has been achieved.     

Reliability evaluation of generating systems containing wind power and energy storage

Global environmental concerns associated with conventional energy generation have led to the rapid growth of wind energy in power systems. Many jurisdictions around the world have set high wind penetration targets in their energy generation mix. Wind speed is variable in nature, and power output from a wind farm is not readily controllable. High wind penetration can lead to high-risk levels in power system reliability and stability. In order to maintain the system stability, wind energy dispatch is usually restricted and energy storage is considered to smooth out the fluctuations and improve supply continuity. The benefits from using energy storage are highly dependent on the operating strategies associated with wind and storage in the power system. A simulation technique that can consider wind farm and energy storage operating strategies is presented. Different operating strategies are compared and the resulting benefits are evaluated. The system impacts of energy storage capacity and operating constraints, wind energy dispatch restrictions, wind penetration level and wind farm location on the reliability benefits from energy storage are illustrated.     

多能互补分布式综合供能系统及典型开发方案研究

多能互补分布式综合供能具有绿色、低碳和高效等典型特征,已经成为国内外能源领域研究与发展的重点。论述了多能互补分布式综合供能系统的基本概念、主要技术特征、系统主要构成与关键技术等,并以国内某典型科技创新园为例,分析其负荷需求和资源禀赋,开发了"五化一体"综合供能实施方案。该方案以燃气分布式、分布式光伏、分布式风电、污水源热泵和储能为供能主体,构建了电力、热力、冷煤水、燃气和中水等能源网络,并借助智能化技术实现供能系统的优化运行,满足不同用能主体用能需求。最后,针对我国多能互补分布式综合供能系统发展现状与存在的问题给出相关建议。     

我国中长期发电供应能力研究

结合我国能源资源储量、禀赋特点和能源发展相关政策,对我国中长期发电供应能力进行了全面的分析,包括燃煤发电、水电、核能发电、风力发电、太阳能发电、生物质能发电和天然气发电的供应能力。在此基础上,以社会总体成本最小为原则,对我国中长期电源结构调整进行了研究,同时研判了我国中长期电源发展布局。     

风光储联合发电运行技术研究

随着能源需求的日益增长和新能源的快速发展,利用风能、太阳能的发电技术已经逐步成熟,且在电网中的渗透率也在不断提高。为弥补风能、太阳能发电所带来的功率不稳定、电能质量低等问题,有必要对风能、太阳能、储能联合发电进行深入研究。文中依据简单平抑方法、考虑一定约束的平抑方法、考虑功率预测与人工智能的平抑方法对储能的平抑控制策略进行了归纳总结。在储能平抑风光波动的研究中滤波算法是最为常见的方法,加入一定的约束会使平抑效果更佳,储能平抑配合精准的预测使整个系统更加平滑。多储能技术混合可以发挥各储能技术优越性。加入储能装置的风光储互补系统可以有效降低原风光互补系统对电网的不利影响。可以在更高程度上平滑风光发电系统的输出特性,增加电网对可再生能源的吸收接纳程度,取得良好的经济和社会效益。     

Optimization of biofuels production via a water–energy–food nexus framework

Biofuels have emerged as an attractive renewable alternative to satisfy the global energy demands. The large-scale production of biofuels requires the installation of biorefining systems that involve strategic decisions for the logistics and operation in the production of biofuels such as location, feedstock type(s), production capacities and interactions with the surrounding environment. This work proposes an optimization framework for the design of a biorefining system while accounting for the interactions with the surrounding watershed using a material flow analysis technique through the design of an efficient supply chain for the production and distribution of feedstocks, grains and biofuels considering the water and land requirements. The proposed model deals with the uncertainty involved in the project (e.g., prices of feedstocks and products, biofuel demands and precipitation in the watershed). A mixed-integer linear programming model is proposed to simultaneously consider the economic and environmental objectives. A case study located in Mexico is solved for a set of scenarios with the purpose of illustrating the capabilities of the proposed optimization approach. The results show strong trade-offs between the considered objectives and the impact of uncertainties.     

燃料电池分布式供能技术发展现状与展望

燃料电池作为一种清洁高效的发电方式,兼具效率高、排放低、安全无噪音等优点,是分布式供能领域的一项重要技术。燃料电池既可以利用传统煤炭、天然气,也可以融合可再生能源实现削峰填谷。在传统煤电领域,散煤的利用是环境污染的重要来源,通过直接碳燃料电池技术,有望解决散煤利用效率低下、污染严重的问题。联合天然气管网,基于燃料电池的微型热电联供系统可实现能源的梯级利用,相比传统的热电分供模式可大大提高能源利用效率。同时,电解池作为燃料电池的逆过程,可将可再生能源富余电力转化为化学能进行储存,实现"三弃"电力的有效转化,在可再生能源的分布式供应系统中具有广阔的发展前景。     

A framework for characterising energy consumption of machining manufacturing systems

Energy consumption in machining manufacturing systems is increasingly of interest due to concern for global climate change and manufacturing sustainability. To utilise energy more effectively, it is paramount to understand and characterise the energy consumption of machining manufacturing systems. To this end, a framework to analyse energy consumption characteristics in machining manufacturing systems from a holistic point of view is proposed in this paper. Taking into account the complexity of energy consumption in machining manufacturing systems, energy flow is described in terms of three layers of machining manufacturing systems including machine tool layer, task layer and auxiliary production layer. Furthermore, the energy consumption of machining manufacturing systems is modelled in the spatial and temporal dimensions, respectively, in order to quantitatively characterise the energy flow. The application of the proposed modelling framework is demonstrated by employing a comprehensive analysis of energy consumption for a real-world machining workshop. The characteristics of energy consumption for machine tool layer, task layer and auxiliary production layer are, respectively, obtained using quantitative models in the spatial and temporal dimensions, which provides a valuable insight into energy consumption to support the exploration of energy-saving potentials for the machining manufacturing systems.     

An overview of power generation in New Zealand

Abstract

Two distinct risks cast a shadow over the sustainability of the existing energy mix. One is security of supply. The diversification of energy sources – both geographical and technological – could mitigate those risks. The second is environmental. Climate change in particular has raised questions about the sustainability of the current fossil fuel emission-intensive mix.

The government is currently investigating whether current R&D priorities and programmes are adequately focused and resourced to bring on-stream the energy technologies of the future. The future energy scenarios explored in the National Energy Strategy will help deliver a view of where some of the potentials and roadblocks currently lie in respect of new energy technologies. This in turn will guide the development of a roadmap of energy research priorities.

Equally important is gaining a better understanding about the actual distribution of research effort on the energy-related technologies and market deployment of different technologies.

While science, research and innovation in a global and highly competitive marketplace coupled with consumer preferences will be a decisive influence, timely access to new technologies and energy resources is of significant national interest.