A Two-Level Approach to Large Mixed-Integer Programs with Application to Cogeneration in Energy-Efficient Buildings

We study a two-stage mixed-integer linear program (MILP) with more than 1 million binary variables in the second stage. We develop a two-level approach by constructing a semi-coarse model (coarsened with respect to variables) and a coarse model (coarsened with respect to both variables and constraints). We coarsen binary variables by selecting a small number of pre-specified daily on/off profiles. We aggregate constraints by partitioning them into groups and summing over each group. With an appropriate choice of coarsened profiles, the semi-coarse model is guaranteed to find a feasible solution of the original problem and hence provides an upper bound on the optimal solution. We show that solving a sequence of coarse models converges to the same upper bound with proven finite steps. This is achieved by adding violated constraints to coarse models until all constraints in the semi-coarse model are satisfied. We demonstrate the effectiveness of our approach in cogeneration for buildings. The coarsened models allow us to obtain good approximate solutions at a fraction of the time required by solving the original problem. Extensive numerical experiments show that the two-level approach scales to large problems that are beyond the capacity of state-of-the-art commercial MILP solvers

Optimal Selection of Distributed Energy Resources under Uncertainty and Risk Aversion

The adoption of small-scale electricity generation has been hindered by uncertain electricity and gas prices. In order to overcome this barrier to investment, we develop a mean-risk optimization model for the long-term risk management problem of an energy consumer using stochastic programming. The consumer can invest in a number of generation technologies, and also has access to electricity and gas futures to reduce its risk. We examine the role of on-site generation in the consumer’s risk management strategy, as well as interactions between on-site generation and financial hedges. Our study shows that by swapping electricity (with high price volatility) for gas (with low price volatility), even relatively inefficient technologies reduce risk exposure and CO _2 emissions. The capability of on-site generation is enhanced through the use of combined heat and power (CHP) applications. In essence, by investing in a CHP unit, a consumer obtains the option to use on-site generation whenever the electricity price peaks, thereby reducing its financial risk. Finally, in contrast to the extant literature, we demonstrate that on-site generation affects the consumer’s decision to purchase financial hedges. In particular, while on-site generation and electricity futures may act as substitutes, on-site generation and gas futures can function as complements

Improving Energy Efficiency in Manufacturing Systems — Literature Review and Analysis of the Impact on the Energy Network of Consolidated Practices and Upcoming Opportunities

Global energy context has become more and more complex in the last decades: raising prices of depleting fossil fuels, together with economic crisis and new international environmental and energy policies, are forcing companies (and manufacturing industry in particular, which is responsible for 90% of industry energy consumptions, in turn making up the 51% of global energy usage, as listed on EIA, the Energy International Agency, website, last accessed on the 5th of October 2014) to cut energy wastes and inefficiencies, and to control their consumptions. Besides the existing analysis of the above mentioned regulatory and economic concerns, Energy Efficiency criticality for manufacturing systems has recently been investigated and proved also by the analysis of its connection with Productivity Efficiency [1-4], which resulted to be strong and mutual, and of the numerous non-energy benefits achieved while performing energy efficiency measures [5], such as the improvement of corporate image and the environmental impact reduction. Over most recent years, Energy Efficiency has therefore become a critical factor for industrial plants’ competitiveness, and is now definitely considered as a key driver to economic development and sustainability. But, despite it all, it is often still difficult for many companies to understand its effectiveness, in good part because of the difficulties met in focusing its technical and economic benefits, as Laitner [6] highlights: “Energy Efficiency has been an invisible resource. Unlike a new power plant or a new oil well, we do not see energy efficiency at work. (...) energy efficiency may be thought of as the cost-effective investments in the energy we do not use either to produce a certain amount of goods and services within the economy.” As a matter of fact, Energy Efficiency still represents a challenging goal for most companies. As above mentioned, numerous problems are yet to overcome in quantifying its benefits and evaluating the cost-effectiveness of related investments, and most of all the huge variety, complexity and changeability of fields, technologies and methodologies involved in its improvement in production systems are responsible for the slowing down of their resolution and of the spread of Energy Efficiency measures and culture. In fact, in order to individuate and prioritize suitable improvement interventions and Energy Efficiency opportunities, and to design and customize the Energy Management System or the Monitoring and Control System according to a particular company’s needs, a deep and complete knowledge of many different subjects and disciplines (ranging from physics and thermodynamics to economy and project management) is needed, besides a good ability and practical sensibility to direct one’s efforts in the right way. Considering that Energy Efficiency isn’t obviously the core business of manufacturing industry, such effort might sometimes be very laborious, and in recent years many companies have decided to demand Energy Management activities to specialized external companies, the so-called Energy Service Companies (ESCos). ESCos generally own the know-how required to individuate Energy Efficiency measures and are also able to fund Energy Efficiency investments (see [7] for a specific literature review); what they usually do not own is a deep understanding of the company’s dynamics, situations and needs, as well as the capability to draw a long-term development path towards the achievement of a diffused Energy Efficiency culture within the company, which shall be consistent with the company’s vision and policies and is essential in order to consolidate and continuously upgrade improvements in such sector. It is then crucial for companies to have at least a general consciousness of all intervention areas and of all possible improvements, both managerial (and/or behavioural) and technological, that could be pursued and achieved, in order to be able to lead their own way towards their sustainable development, and also to capitalize ESCOs’ assistance and services. In order to overcome part of these difficulties, and in particular to make it easier for companies to address their efforts and catch best efficiency opportunities, a logical and systemic approach is necessary: it would help not to overlook any possible area of improvement, to easily classify and understand those areas, but also to identify the most suitable and cost-effective, and eventually to prioritize them. In the light of this, some studies have already been conducted in order to find out methods and tools to assess the current level of maturity of a company in the Energy Management field [8], and to help individuating a possible development path. However, although they point out some possible development scenarios, they do not provide a complete and organic categorization of all possible areas of intervention, so as to make it easier for practitioners to address their efforts into the right way. In this chapter, a new conceptual scheme to organize and classify Energy Efficiency measures is defined, leading from the definition of Energy Cost per Product Unit and further breaking it up in order to identify and define all possible areas of intervention, providing for each of them a brief overview of possible measures and opportunities and a specific literature review. All scientific papers, books and technical papers considered for the literature review of each area (chosen on the basis of a wide literature research and on authors’ on-field experience) are recalled and systematized in Table 1, so that the reader is guided through their examination and rapidly addressed to their consultation. In addition, a qualitative evaluation of the impact of some possible Energy Efficiency measures from each area on the energy network is given, in order to give both practitioners and researchers a first input to further focus on this additional feasibility evaluation criteria for Energy Efficiency measures, which enables to evaluate them on a national or international level rather than considering the benefits or concerns belonging to a single company

An integrated simulation tool proposed for modeling and optimization of CHP units

Master's thesis in Petroleum engineeringIn this project, a novel framework for CHP optimization is proposed. The objective of the study was to develop an automatic optimization tool based on the integration of IPSEpro simulation software and MATLAB programming environment. The data exchange between these components was organized via COM interface. An experimentally validated model of the commercial AET100 CHP unit was utilized. The CHP was considered as a part of a grid. Therefore electricity trading possibility was taken into account. The system was extended to polygeneration by implementing a solar panel as an additional power source. The objective was to minimize the cost function, which consists of operational and capital investments costs, under a set of constraints. For solving the problem, the Genetic Algorithm was applied. As an addition to the study, two other algorithms (Particle Swarm Optimization and Differential Evolution) were also tested. The applying a tool to real data was not considered in the project. However, an optimization was done for test data to show the performance of a developed framework. The test optimization was done for the 24-hours period in July and December, with different electricity and gas price profiles and various ambient conditions. The obtained results were analyzed in details. It was shown that the proposed optimization tool provides appropriate results. It is flexible and has a good potential to be further extended and developed

Modelling environment for the design and optimisation of energy polygeneration systems

The optimal design and operation of an energy supply system is very important for the matching of the energy production and consumption especially in the residential-tertiary sector characterized by an energy demand with a high variability. The main objective of this thesis is to develop an optimisation environment for the preliminary design and analysis of polygeneration plants. The optimisation models are organized in different units represented by blocks that can be connected between each other to create the flowsheet of the polygeneration system. To characterize the energy demand in the residential and tertiary sector a graphic methodology has been developed to select typical energy demand days from a yearly energy demand profile. The environment developed has been applied to two case studies: a small scale polygeneration plant using a liquid desiccant system for air conditioning and a polygeneration plant connected to a district heating and cooling network

IEA ECES Annex 31 Final Report - Energy Storage with Energy Efficient Buildings and Districts: Optimization and Automation

At present, the energy requirements in buildings are majorly met from non-renewable sources where the contribution of renewable sources is still in its initial stage. Meeting the peak energy demand by non-renewable energy sources is highly expensive for the utility companies and it critically influences the environment through GHG emissions. In addition, renewable energy sources are inherently intermittent in nature. Therefore, to make both renewable and nonrenewable energy sources more efficient in building/district applications, they should be integrated with energy storage systems. Nevertheless, determination of the optimal operation and integration of energy storage with buildings/districts are not straightforward. The real strength of integrating energy storage technologies with buildings/districts is stalled by the high computational demand (or even lack of) tools and optimization techniques. Annex 31 aims to resolve this gap by critically addressing the challenges in integrating energy storage systems in buildings/districts from the perspective of design, development of simplified modeling tools and optimization techniques