OPTIMUM DESIGN AND OPERATION OF COMBINED COOLING HEATING AND POWER SYSTEM WITH UNCERTAINTY

Combined cooling, heating, and power (CCHP) systems utilize renewable energy sources, waste heat energy, and thermally driven cooling technology to simultaneously provide energy in three forms. They are reliable by virtue of main grid independence and ultra-efficient because of cascade energy utilization. These merits make CCHP systems potential candidates as energy suppliers for commercial buildings. Due to the complexity of CCHP systems and environmental uncertainty, conventional design and operation strategies that depend on expertise or experience might lose effectiveness and protract the prototyping process. Automation-oriented approaches, including machine learning and optimization, can be utilized at both design and operation stages to accelerate decision-making without losing energy efficiency for CCHP systems. As the premise of design and operation for the combined system, information about building energy consumption should be determined initially. Therefore, this thesis first constructs deep learning (DL) models to forecast energy demands for a large-scale dataset. The building types and multiple energy demands are embedded in the DL model for the first time to make it versatile for prediction. The long short-term memory (LSTM) model forecasts 50.7% of the tasks with a coefficient of variation of root mean square error (CVRMSE) lower than 20%. Moreover, 60% of the tasks predicted by LSTM satisfy ASHRAE Guideline 14 with a CVRMSE under 30%. Thermal conversion systems, including power generation subsystems and waste heat recovery units, play a vital role in the overall performance of CCHP systems. Whereas a wide choice of components, nonlinear characteristics of these components challenge the automation process of system design. Therefore, this thesis second designs a configuration optimization framework consisting of thermodynamic cycle representation, evaluation, and optimizer to accelerate the system design process and maximize thermal efficiency. The framework is the first one to implement graphic knowledge and thermodynamic laws to generate new CO2 power generation (S-CO2) system configurations. The framework is then validated by optimizing the S-CO2 system's configurations under simple and complex component number limitations. The optimized S-CO2 system reaches 49.8% thermal efficiency. This efficiency is 2.3% higher than the state of the art. Third, operation strategy with uncertainty for CCHP systems is proposed in this thesis for a hospital with a floor area of 22,422 m2 at College Park, Maryland. The hospital energy demands are forecasted from the DL model. And the S-CO2 power subsystem is implemented in CCHP after optimizing from the configuration optimizer. A stochastic approximation is combined with an autoregression model to extract uncertain energy demands for the hospital. Load-following strategies, stochastic dynamic programming (SDP), and approximation approaches are implemented for CCHP system operation without and with uncertainties. As a case study, the optimization-based operation overperforms the best load-following strategy by 14% of the annual cost. Approximation-based operation strategy highly improves the computational efficiency of SDP. The daily operating cost with uncertain cooling, heating, and electricity demands is about 0.061 $/m2, and a potential annual cost is about 22.33$/m2. This thesis fills the gap in multiple energy types forecast for multiple building types via DL models, prompts the design automation of S-CO2 systems by configuration optimization, and accelerates operation optimization of a CCHP system with uncertainty by an approximation approach. In-depth data-driven methods and diversified optimization techniques should be investigated further to boost the system efficiency and advance the automation process of the CCHP system

Market and Economic Modelling of the Intelligent Grid: End of Year Report 2009

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy

A Technology Selection and Operation (TSO) optimisation model for distributed energy systems: Mathematical formulation and case study

This paper presents a model which simultaneously optimises the selection and operation of technologies for distributed energy systems in buildings. The Technology Selection and Operation (TSO) model enables a new approach for the optimal selection and operation of energy system technologies that encompasses whole life costing, carbon emissions as well as real-time energy prices and demands; thus, providing a more comprehensive result than current methods. Utilizing historic metered energy demands, projected energy prices and a portfolio of available technologies, the mathematical model simultaneously solves for an optimal technology selection and operational strategy for a determined building based on a preferred objective: minimizing cost and/or minimizing carbon emissions. The TSO is a comprehensive and novel techno-economic model, capable of providing decision makers an optimal selection from a portfolio of available energy technologies. The current portfolio of available technologies is composed of various combined heat and power (CHP) and organic Rankine cycle (ORC) units. The TSO model framework is data-driven and therefore presents a high level of flexibility with respect to time granularity, period of analysis and the technology portfolio. A case study depicts the capabilities of the model; optimisation results under different temporal arrangements and technology options are showcased. Overall, the TSO model provides meaningful insights that allow stakeholders to make technology investment decisions with greater assurance

Optimal operation planning of Distributed Energy Systems through multi-objective approach: a new sustainability-oriented pathway

The energy system is an essential part of nowadays society. The concept of “energy system” commonly refers to the energy-supply chain as the whole system consisting of the energy conversion devices as well as storage units from the energy resources to the final user demands. In the 1900’s, energy has been commonly provided by large generation power plants operating in a central location and transmitted to consumers via transmission and distribution networks. In a typical centralized energy system, a large number of end-users is located within a large area. A Distributed Energy System (DES) can be regarded as the opposite of a centralized energy system, where the term “distributed” illustrates how single energy conversion devices and storage units are integrated into the whole energy system. Therefore, a DES refers to an energy system, where energy is made available close to energy consumers, typically relying on a number of small-scale technologies. In recent years, developing DESs has attracted much interest, since these systems have been recognized as a sustainability-oriented alternative to conventional centralized energy systems. In general, sustainability means an equitable distribution of the limited resources and opportunities in the context of the economy, the society, and the environment, aiming at the well-being of everyone, now and in future, thereby guaranteeing that needs of future generations may be completely satisfied as happens today. One of the main benefits of DESs is the possibility to integrate different energy resources, including renewable ones, as well as to recover waste heat from power generation plants for thermal purposes. This benefit allows to enhance sustainability of the energy supply through a more efficient use of the energy resources as well as a reduced environmental impact, as compared with conventional energy supply systems. Through an appropriate planning, DESs may exhibit even better performances than a single polygeneration system, such as Combined Cooling Heating and Power systems or conventional energy supply systems. The optimal planning of DESs is not a trivial task, as integration of different types of energy resources and energy conversion devices as well as storage units may increase the complexity of the system. Moreover, generally different stakeholders participate in DESs development and management. Hence, objectives can be defined from different perspectives, such as the developers and operators of DESs, or the civil society, ideally represented by the regulator. Some of the DESs planning objectives are naturally conflicting. Consequently, there is not a single planning solution, which can satisfy all the stakeholders. For instance, society interest in sustainable energy supply systems, and with low environmental impacts, might conflict with the economic interest of the developers and operators of DESs. A multi-objective approach helps to identify compromise solutions, which benefit all the stakeholders. This thesis presents an original tool based on a mathematical programming approach, to attain the optimal operation planning of DESs through multi-objective criteria, by considering both short- and long-run priorities. Multi-objective optimization problems are formulated to find the optimized operation strategies of DESs in order to take into account short-run priorities characterized by the crucial economic factor, as well as long-run priorities in terms of sustainability. This latter is attained through exergy concepts as well as environmental impacts assessments

Initialization of a Multi-objective Evolutionary Algorithms Knowledge Acquisition System for Renewable Energy Power Plants

pp. 185-204The design of Renewable Energy Power Plants (REPPs) is crucial not only for the investments' performance and attractiveness measures, but also for the maximization of resource (source) usage (e.g. sun, water, and wind) and the minimization of raw materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te) consumption. Hence, several appropriate and satisfactory Multi-objective Problems (MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only be managed by very well organized knowledge acquisition on all REPPs' design equations and models. The proposed MOPs need to be solved with one or more multiobjective algorithm, such as Multi-objective Evolutionary Algorithms (MOEAs). In this respect, the first aim of this research study is to start gathering knowledge on the REPPs' MOPs. The second aim of this study is to gather detailed information about all MOEAs and available free software tools for their development. The main contribution of this research is the initialization of a proposed multi-objective evolutionary algorithm knowledge acquisition system for renewable energy power plants (MOEAs-KAS-FREPPs) (research and development loopwise process: develop, train, validate, improve, test, improve, operate, and improve). As a simple representative example of this knowledge acquisition system research with two selective and elective proposed standard objectives (as test objectives) and eight selective and elective proposed standard constraints (as test constraints) are generated and applied as a standardized MOP for a virtual small hydropower plant design and investment. The maximization of energy generation (MWh) and the minimization of initial investment cost (million €) are achieved by the Multi-objective Genetic Algorithm (MOGA), the Niched Sharing Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all proposed standardized MOEAs on two desktop computer configurations (Windows 10 Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB RAM with internet connection). The algorithm run-times (computation time) of the current applications vary between 20.64 and 59.98 seconds.S

Practical Optimisation of District Energy Systems: Representation of Technology Characteristics, Demand Uncertainty, and System Robustness

District energy systems are an alternative to conventional national-scale networks to meet demand in urban areas. Decentralising electricity production reduces distribution losses, while centralising thermal energy production capitalises on economies of scale. Furthermore, geographic proximity facilitates the use of waste heat from electricity production to meet thermal demand. However, it is difficult to assess which configuration of technologies will best suit a cluster of users. Mathematical optimisation techniques have been extensively researched as a method to resolve this, as they can simplify the design of investment portfolios and operation schedules for a given set of geolocated demands. However, they are not yet practically applicable. This thesis uses the open-source, mixed integer linear programming framework Calliope to present three methodological enhancements which address model simplification, parameter uncertainty, and conflicting decision-maker objectives. These enhancements enable the practical design of district energy systems through data-centric workflows which can readily represent real system complexities in a tractable optimisation model. This thesis first examines the impact of parameter simplification in a linear model. Piecewise linearisation is applied to nonlinear part-load energy consumption curves and a pre-processing step is developed to optimise breakpoint positioning along a piecewise curve. Second, a three-step method is proposed to handle demand uncertainty in linear models, using historical demand data. These steps are scenario generation, scenario reduction, and scenario optimisation. Using out-of-sample tests, robustness of optimal investments to unmet demand is quantified. Furthermore, system resilience to unexpected events is explored by introducing interruptions to the national electricity grid availability for a district in Bangalore, India. Scenario optimisation is extended to account for these interruptions as well as to mitigate unfavourably high levels of CO$_2$ emissions in system design. Finally, this thesis identifies eight possible decision makers, who each hold a different objective in the design of a district energy system. Optimal technology configuration and out-of-sample test results are compared across all decision-maker objectives. These methodological enhancements demonstrate the capability of optimisation models to be reflective of reality whilst being transparent concerning the impact of simplifications, uncertainty, and conflicting objectives. Calliope is extended in this thesis to be practically applicable for district energy systems. Moreover, its extensibility facilitates the continuation of development, including possible future work into data validation and spatial dimension simplification.This research was supported by the Engineering and Physical Sciences Research Council (grant EP\L016095\1). This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (http://www.csd3.cam.ac.uk/), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP\P020259\1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk)

Integrated performance based design of communities and distributed generation

The vertically integrated utility market within the U.S. is undergoing rapid changes due to the rise of small-scale distributed power generation known as microgrids, which are local networks of power generation and distribution typically serving a demand less than 40 MW. Primary drivers for microgrid investment are the performance benefits these systems return to their owners, which include increased reliability, reduced emissions and reduced operating costs. We define a novel modeling methodology to represent the microgrid as an integrated system of the demand and supply. Previous work to develop an integrated system model does not adequately model the building thermal demand, incorporate a modeler’s knowledge of the grid’s availability or allow for a user to model their tolerance for unmet demand. To address these modeling issues, we first demonstrate a technique for representing a building stock as a reduced order hourly demand model. Next, as demand side measures are typically defined at the building level as discrete options, we demonstrate a technique for converting a large discrete optimization problem into a simplified continuous variable optimization problem through the use of Pareto efficient cost functions. The reduced problem specification results in 90% fewer function evaluations for a benchmark optimization task. Then, we incorporate two new features into the Distributed Energy Resource Customer Adoption Model (DER-CAM) developed by Lawrence Berkeley National Laboratory (LBNL) that allow users to define grid outage scenarios and their limit of expected energy demand not served. Applying the integrated model to a microgrid design scenario return solutions that exhibit on average an 8% total annual cost reduction and 18% reduction in CO2 emissions versus a Supply Only case. Similarly, the results on average reduce total annual cost by 5% and annual emissions by 17% for a Demand First case. In summary, we present a modeling methodology with application to joint decision making that involve renewable power supply, building systems and passive building design measures and recommend this model for performance based microgrid design.Ph.D