limits and potentials of mixed integer linear programming methods for optimization of polygeneration energy systems

Abstract The simultaneous production of different energy vectors from hybrid polygeneration plants is a promising way to increase energy efficiency and facilitate the development of distributed energy systems. The inherent complexity of polygeneration energy systems makes their economic, environmental and energy performance highly dependent on system synthesis, equipment selection and capacity, and operational strategy. Mixed Integer Linear Programming (MILP) is the state of the art approach to tackle the optimization problem of polygeneration systems. The guarantee of finding global optimality in linear problems and the effectiveness of available commercial solvers make MILP very attractive and widely used in optimization problems of polygeneration systems. Nevertheless, several drawbacks affect the MILP formulation, such as: the impossibility of taking into account nonlinear effects; the necessity of considering all the time periods at once; the risk of high-dimensionality of the problem. To tackle these limitations, several techniques have been developed, such as: piecewise linearization methods; rolling horizon approaches; dimensionality reduction by means of energy demands clustering algorithms. In this paper, limits and potentials of MILP methods for the optimization problem of polygeneration energy systems are reviewed and discussed

Optimal integrated sizing and operation of a CHP system with Monte Carlo risk analysis for long-term uncertainty in energy demands

In this study a probabilistic approach for optimal sizing of cogeneration systems under long-term uncertainty in energy demand is proposed. A dynamic simulation framework for detailed modeling of the energy system is defined, consisting in both traditional and optimal operational strategies evaluation. A two-stage stochastic optimization algorithm is developed, adopting Monte Carlo method for the definition of a multi-objective optimization problem. An Italian hospital facility has been used as a case study and a gas internal combustion engine is considered for the cogeneration unit. The results reveal that the influence of uncertainties on both optimal size and annual total cost is significant. Optimal size obtained with the traditional deterministic approach are found to be sub-optimal (up to 30% larger) and the predicted annual cost saving is always lower when accounting for uncertainties. Pareto frontiers of different CHP configurations are presented and show the effectiveness of the proposed method as a useful tool for risk management and focused decision-making, as tradeoffs between system efficiency and system robustness

Synthesis and Optimal Operation of Smart Microgrids Serving a Cluster of Buildings on a Campus with Centralized and Distributed Hybrid Renewable Energy Units

Micro-district heating networks based on cogeneration plants and renewable energy technologies are considered efficient, viable and environmentally-friendly solutions to realizing smart multi-energy microgrids. Nonetheless, the energy production from renewable sources is intermittent and stochastic, and cogeneration units are characterized by fixed power-to-heat ratios, which are incompatible with fluctuating thermal and electric demands. These drawbacks can be partially overcome by smart operational controls that are capable of maximizing the energy system performance. Moreover, electrically driven heat pumps may add flexibility to the system, by shifting thermal loads into electric loads. In this paper, a novel configuration for smart multi-energy microgrids, which combines centralized and distributed energy units is proposed. A centralized cogeneration system, consisting of an internal combustion engine is connected to a micro-district heating network. Distributed electric heat pumps assist the thermal production at the building level, giving operational flexibility to the system and supporting the integration of renewable energy technologies, i.e., wind turbines, photovoltaic panels, and solar thermal collectors. The proposed configuration was tested in a hypothetical case study, namely, a University Campus located in Trieste, Italy. The system operation is based on a cost-optimal control strategy and the effect of the size of the cogeneration unit and heat pumps was investigated. A comparison with a conventional configuration, without distributed heat pumps, was also performed. The results show that the proposed configuration outperformed the conventional one, leading to a total-cost saving of around 8 %, a carbon emission reduction of 11 %, and a primary energy saving of 8 %

Optimal design and operation of cogeneration-based distributed energy systems (with a special focus on the integration of heat pumps)

Distributed Energy Systems (DESs) based on polygeneration plants can play an important role in the development of a more sustainable energy paradigm. The high energy efficiency resulting from the optimal integration of different energy technologies and vectors, and the high penetration of renewable energy sources allow for significant primary energy savings and reduced greenhouse gas emissions, while guaranteeing the economic sustainability. Nevertheless, the inherent complexity of DESs makes the economic, environmental and energy performance highly dependent on equipment capacity and operational strategy, thus requiring the development and adoption of advanced design methodologies and optimization tools. Moreover, the availability of many different energy technologies entails the issue of the selection and layout of the components to be installed in the system. The synthesis and evaluation of novel configurations of DESs is a rich and complex topic, which is worthy to be investigated. In particular, the integration of heat pumps, which represent a link between different energy vectors, can be especially favorable in DESs, by providing operational flexibility and increasing the overall energy efficiency. In this framework, this thesis aims to contribute to the development of the scientific knowledge in the field of DESs, by pursuing three main objectives: (i) a systematic overview of simulation-based approaches for the design of DESs, (ii) the development of methodologies for the optimal synthesis, sizing, and operation of DESs, and (iii) the proposal and evaluation of novel configurations of DESs. An overview of cogeneration-based DESs and related energy technologies is given, and benefits and drawbacks compared to traditional energy systems are discussed. The models and simulation of DESs are presented, as well as the objectives and methodologies of this approach. The issue of uncertainty in DESs is dealt with, identifying uncertain parameters in DESs and presenting probabilistic models and simulation techniques, such as the Monte Carlo method. Moreover, the topic of the design of DESs is thoroughly analyzed. The synthesis-sizing-operation paradigm is defined, and both deterministic and stochastic indicators for evaluating DES performance are presented. Then, a comprehensive review of traditional and innovative methodologies for the definition of operational strategies and design techniques is given. In this regard, a wide-ranging selection of literature works is presented and discussed. Closely related to the topic of simulation-based design of DESs, a complete overview of optimization techniques adopted in this field is given. Single-objective optimization methods are presented, with a focus on genetic algorithm and mixed integer linear programming. Then, the multi-objective optimization problem and scalarizing functions are introduced, with a highlight on the achievement function approach. Furthermore, optimization-under-uncertainty approaches and dimensionality reduction methods (namely data clustering and rolling horizon) are discussed. In the second part of the thesis, innovative methodologies for the optimal design of cogeneration-based DESs are developed and tested in case studies. A comprehensive methodology for the integrated optimal sizing and operation of cogeneration systems with thermal energy storage is defined. A probabilistic approach to consider long-term uncertainty in energy demands in the optimal design of cogeneration system is proposed. An operational optimization method for trigeneration system based on real-time measurements of energy demands and ambient conditions is developed. Furthermore, innovative system configurations are proposed and evaluated, with a view to increase the overall energy efficiency and penetration of renewable energy sources, mainly focusing on the integration of heat pump technologies in polygeneration systems. To this end, exergy and levelized cost of energy analysis are performed, and optimization tools are adopted to evaluate the optimal design of the proposed configurations in case studies. A novel trigeneration system including a high-temperature vapor-compression heat pump is investigated. The high-efficiency integration of reversible absorption heat pumps and internal combustion engine is examined. Vapor-compression heat pumps are integrated within a hybrid renewable trigeneration system

Modellazione e analisi sperimentale dell’effetto di getti elettroidrodinamici su ebollizione nucleata e flusso termico critico

L’impiego di getti indotti in un fluido dielettrico tramite l’iniezione di ioni da un elettrodo metallico a punta si è dimostrato essere un efficiente metodo attivo di miglioramento dello scambio termico, a fronte di un’esigua richiesta di potenza. Lo scopo del presente lavoro è indagare l’effetto di tali getti di origine elettroidrodinamica sullo scambio termico in regime di ebollizione nucleata e sul flusso termico critico. È stata innanzitutto condotta un’ampia analisi della letteratura esistente, conclusasi con la presentazione di un modello preliminare, atto a descrivere e prevedere la condizione di flusso termico critico in presenza di getto impattante di origine elettroidrodinamica. Successivamente si è proceduto con una campagna sperimentale finalizzata ad indagare l’effetto della presenza di getti ionici sul regime di ebollizione nucleata satura, con fluido di lavoro FC-72 e configurazione degli elettrodi di alta tensione del tipo punta-piano, con una tensione massima di circa 29 kV. È stato osservato un significativo incremento dello scambio termico su tutto il regime indagato; inoltre, si è rilevata la capacità del getto ionico di ritardare a flussi termici e sovratemperature di parete maggiori il verificarsi del flusso termico critico. È stata infine svolta un’analisi delle caratteristiche del getto ionico ottenuto e di alcune proprietà elettroidrodinamiche del fluido

Integration of reversible absorption heat pumps in micro-trigeneration systems: application to an office building

The present paper investigates the integration of reversible absorption heat pumps in a novel micro-trigeneration system. Differently from traditional absorption chillers, reversible absorption heat pumps can be driven by the exhaust gas of an internal combustion engine to produce both cooling and heating, depending on the season. This technology may be particularly interesting in residential and commercial buildings with low and medium temperature emission systems. Detailed models of all the subsystems are given. Sizing and lifetime operation of the proposed system are stochastically optimized by means of a genetic algorithm coupled with Monte Carlo simulations for a case study, namely an office building located in central Italy. Energy demands are evaluated by means of a dynamic simulation, validated on real energy data. Results show that the integration of the reversible absorption heat pump provides valuable economic and energy performance. Traditional trigeneration and cogeneration systems and a separate-production system have 2%, 4% and 7% higher net present costs, respectively, also employing 4%, 9% and 11 % more primary energy. The proposed design is proven to be robust to uncertainties on the electrical, heating and cooling loads, giving a 26% reduction of the relative uncertainty range on the output net present cost and matching, in any case, the building energy demands

Integration of reversible absorption heat pumps in cogeneration systems: Exergy and economic assessment

Polygeneration energy systems in building applications are widely recognized as an effective way to reduce primary energy consumption and greenhouse gas emissions, thanks to high energy efficiencies and optimal integration of different energy technologies and sources. In the present work, the integration of a reversible absorption heat pump and an internal combustion engine in a novel trigeneration system is proposed. The reversible absorption heat pump, which employs a water-ammonia mixture, is driven by the exhaust gas of the engine, and can produce heating and cooling, alternately. The proposed trigeneration system is presented, and the energy services provided under the heating and cooling operating modes are evaluated. A levelized cost of energy analysis is conducted to evaluate the economic viability of the proposed system. Next, a second-law analysis compares its overall exergy efficiency to those of conventional systems. Finally, the novel trigeneration system is implemented in a case study, namely a large office building located in Pisa, Italy. The integrated optimal sizing and operation are evaluated by using a genetic algorithm-based procedure. The findings show that the system integrating reversible absorption heat pump and cogeneration unit provides valuable economic and energy performance. The exergy efficiency of the system can reach 43%, and cost savings of around 5% and 10% are achieved compared to traditional cogeneration and separate-production system, respectively

A comprehensive methodology for the integrated optimal sizing and operation of cogeneration systems with thermal energy storage

Cogeneration systems are widely acknowledged as a viable solution to reduce energy consumption and costs, and CO 2 emissions. Nonetheless, their performance is highly dependent on their capacity and operational strategy, and optimization methods are required to fully exploit their potential. Among the available technical possibilities to maximize their performance, the integration of thermal energy storage is recognized as one of the most effective solutions. The introduction of a storage device further complicates the identification of the optimal equipment capacity and operation. This work presents a cutting-edge methodology for the optimal design and operation of cogeneration systems with thermal energy storage. A two-level algorithm is proposed to reap the benefits of the mixed integer linear programming formulation for the optimal operation problem, while overcoming its main drawbacks by means of a genetic algorithm at the design level. Part-load effects on nominal efficiency, variation of the unitary cost of the components in relation to their size, and the effect of the storage volume on its thermal losses are considered. Moreover, a novel formulation of the optimization problem is proposed to better characterize the heat losses and operation of the thermal energy storage. A rolling-horizon technique is implemented to reduce the computational time required for the optimization, without affecting the quality of the results. Furthermore, the proposed methodology is adopted to design a cogeneration system for a secondary school in San Francisco, California, which is optimized in terms of the equivalent annual cost. The results show that the optimally sized cogeneration unit directly meets around 70% of both the electric and thermal demands, while the thermal energy storage additionally covers 16% of the heat demands

Real-time operational optimization of a complex DHC plant

Performance of polygeneration systems connected to district heating and cooling networks are highly dependent on the operational strategy. The development of advanced control algorithms for real-time operations of CCHP systems must deal with several issues, such as uncertainties in energy demand and weather forecast, non-linear part-load performances, multiple time-varying loads. In this paper, an operational optimization method for a complex DHC plant is proposed. The method is based on the moving average of real-time measurements of energy load demands and ambient conditions, overcoming the need for weather forecasts and a model for the estimation of future load demands. The proposed algorithm is tested with real energy demand data from a DHC close to Barcelona. A complex polygeneration system is considered, including an internal combustion engine, a double-effect absorption chiller, an electric chiller, a boiler and a cooling tower. Part-load behaviour of the components and ambient condition effects are considered to provide a detailed modelling of the system. Results of the real-time optimal control are presented and compared to those of traditional operational strategies

An operational optimization method for a complex polygeneration plant based on real-time measurements

The combined production of electricity, heat and cold by a polygeneration system connected to a district heating and cooling network can provide high energy utilization efficiency. The inherent complexity of simultaneous production of different services and the high variability in the energy demand make combined cooling and heating systems performance highly dependent on the operational strategy. In this paper, an operational optimization method based on the moving average of real-time measurements of energy demands and ambient conditions is proposed. Real energy demand data from a district heating and cooling network close to Barcelona, Spain, are used to test the method. A complex polygeneration system is considered, consisting of an internal combustion engine, a double-effect absorption chiller, an electric chiller, a boiler and a cooling tower. A detailed modelling of the system is provided, considering partial load behavior of the components and ambient conditions effects. Results of the real-time optimal management are discussed and compared to traditional operational strategies and to the ideal optimal management achievable with perfectly accurate forecast of energy demands. Moreover, the optimal width of the window adopted for the moving average of real-time data is identified