بهینه سازی چند هدفه چند دوره ای از فن آوری تبدیل زیست توده با استفاده از الگوریتم های تکاملی و برنامه ریزی خطی مخلوط عدد صحیح (MILP)

The design and operation of energy systems are key issues for matching energy supply and demand. A systematic procedure, including process design and energy integration techniques for sizing and operation optimization of poly-generation technologies is presented in this paper. The integration of biomass resources as well as a simultaneous multi-objective and multi-period optimization, are the novelty of this work. Considering all these concepts in an optimization model makes it difficult to solve. The decomposition approach is used to deal with this complexity. Several options for integrating biomass in the energy system, namely back pressure steam turbines, biomass rankine cycles (BRC), biomass integrated gasification gas engines (BIGGE), biomass integrated gasification gas turbines, production of synthetic natural gas (SNG) and biomass integrated gasification combined cycles (BIGCC), are considered in this paper. The goal is to simultaneously minimize costs and CO2 emission using multi-objective evolutionary algorithms (EMOO) and Mixed Integer Linear Programming (MILP). Finally the proposed model is demonstrated by means of a case study. The results show that the simultaneous production of electricity and heat with biomass and natural gas are reliable upon the established assumptions. Furthermore, higher primary energy savings and CO2 emission reduction, 40%, are obtained through the gradual increase of renewable energy sources as opposed to natural gas usage. However, higher economic profitability, 52%, is achieved with natural gas-based technologies.

In the perspective of increasing the share of renewable energies to mitigate global warming and with respect to the global issue of sustainable energy development, biomass has been increasingly focused on as a potential source of renewable energy. Poly-generation technologies, joined with the integration of biomass, have a good potential for CO2 emissions reduction. Table 1 compares the lifecycle CO2 emissions of biomass with other resources and shows the potential of biomass for CO2 emissions reduction. This data is selected from the ecoinvent life cycle inventories database [1]. ΔCO2 in Table 1 shows the CO2 emissions of resources minus the CO2 emissions of biomass. The biogenic carbon captured by photosynthesis is not accounted for the biomass CO2 emissions. In addition, if any CO2 mitigation technology is adopted [2], negative CO2 emissions will be realized, which can reduce the emissions in the atmosphere [3]. Table 1. CO2 Intensity of resources. Resources CO2 − Eq [1]: [kg/MJ] ΔCO2 [kg/MJ] Electricity 0.3155 0.3071 Natural gas 0.0725 0.0641 Biomass 0.0084 0 Table options In the present work, several options for integrating biomass in a poly-generation plant are studied, however before going forwards, a systematic optimization procedure is needed to select and size the equipments. The optimization of energy systems that include one or more technologies to meet the requirements of energy systems is extensively studied by many authors. It is referred to [4] for a detailed overview. From the author’s point of view, the majority of studies can be divided into two main categories; the first category includes thermo-economic simulations and synthesis of biomass technologies and the second category includes optimization techniques for selecting and sizing equipments. Researchers have paid much attention in the literature on thermo-economic simulations and synthesis of biomass technologies in cogeneration plants. A trigeneration system using a heat engine and a vapour compression chiller, running on biofuel, is simulated in [5] and a comparative analysis between the biofuel trigeneration and conventional fossil fuel was carried out. The energy and the exergy efficiencies of trigeneration system consisting of a biomass combustor, an ORC, a single-effect absorption chiller, and a heat exchanger are studied in [6] through a simulation. Process integration methodology and simulation is applied in [7] to deal with an application of a heat pump in energy systems for biomass gasification in a wood processing plant. For a detailed overview, simulation and modelling of biomass based cogeneration systems are reviewed in [8]. Most of these publications carried out only simulations, while system design optimization is neglected. The second state of the art part of this work is on optimization techniques for selecting and sizing equipments. Diverse procedures exist to size cogeneration plants, like a structural optimization approach based on the mixed-integer linear programming [9]. Lyer and Grossmann [10] conducted a work on utility systems optimization for a multi-period operating condition by using a MILP method, however it was limited to the steam network model. Other researchers [11] developed a mono objective optimization model for the integration of cooling and heating systems based on the process integration and temperature intervals. A mono objective operations optimization and the design of trigeneration plants is also studied in [12]. Some limitations related to the simultaneous consideration of the economic evaluation and the CO2 emissions assessment may appear in available optimization methods developed in [13] and [14]. Moreover, a mathematical programing model for selection and sizing of alternative equipments in a poly-generation scheme was investigated by researchers [15]. Three algorithms based on evolutionary and/or social metaphors for mono objective energy systems optimization problems were studied in [16]. An optimization tool for a district energy system design is presented in [17]. For a detailed overview, the role of optimization modelling techniques in power generation is reviewed in [18]. However, most of these optimization models only included a mono economic objective function, completed with environmental and energetic targets as constraints, rather than multi-objective optimization, as is done here. To sum up, energy system analyses are extensively studied by many authors. However, a systematic procedure including process design and energy integration techniques with simultaneous consideration of multi-periods and multi-objective aspects for energy system designs is still missing. Considering all these concepts in a single optimization model defines a Mixed Integer non-Linear Problem (MINLP) with non differentiable equations due to the use of the temperature as decision variable in heat cascade constraints. The purpose of the developed model in this paper is to use the decomposition approach to deal with this complexity. In order to do so, a multi-objective optimization model with evolutionary algorithms (EMOO) and MILP has been developed (sec.2). In the developed model the features of both above mentioned types of study; the integration of biomass technologies’ simulation models in the energy system as well as multi-objective optimization for sizing a cogeneration plant, are combined in a systematic procedure. This procedure evaluates the total costs and the CO2 emissions simultaneously by decomposing the model into master and slave optimizations [19]. The considerations of several equipments as well as their thermodynamic properties (sec.3), including process design and energy integration techniques with simultaneous consideration of multi-periods and multi-objective aspects (sec.2.3), are important advantages in the present work. Finally, the developed model is demonstrated by means of a case study (sec.4). Results are compared to conclude advantages and disadvantages of alternative solutions (sec.4.1). The energy system analyses could be divided into two major steps; first sizing and design optimization and second, operation optimization. The developed model in this paper is mainly used for the conceptual design and sizing optimization. The system configuration is optimized in this step. After that, the operation optimization will be done with more detailed modelling including a storage system, part load efficiency and advance control system by fixing the system configuration as an input data. Never the less this detail operation optimization is only possible if a feasible solution for a system configuration is obtained in the first step.

A method for the preliminary design of integrated urban energy systems has been presented in this paper. A systematic procedure including process design and energy integration techniques with simultaneous consideration of multi-periods and multi-objective aspects, economic and environment targets, for energy system design is proposed. It combines the use of optimization techniques with a data base of thermo-economic models that are used to build conversion technologies superstructures. Considering all these concepts in a single optimization model makes it difficult to solve. In this study, a decomposition approach is used to deal with this complexity. In order to do so, a multi-objective optimization model using evolutionary algorithms (MOO) and MILP model has been developed. The developed model is mainly used for the preliminary conceptual design and sizing optimization, where the system configuration is optimized based on the economic and environmental targets. The model is used to study the large scale integration of renewable energy like biomass in district heating systems. The results will be used to propose energy conversion systems configurations that will have to be optimized in more detail before envisaging the optimal operation strategies. The illustrative example demonstrates the ability of the developed method to solve a real scale problem. In the example, the energy system is required to meet the heat demands of an urban area while considering both economic and environmental objectives. Furthermore, power generation is considered as an opportunity for the utility company. From the results it appears that natural gas is less attractive than biomass when CO2 taxes are included, but it is sensitive to the CO2 taxes and the resources price. However, higher economic profitability (up to 52%) is yet achieved with natural gas-based technologies. Furthermore, higher primary energy savings and CO2 emission reduction (up to 40%) are obtained through the gradual increase of biomass usage. The comparison between the carnot composite curves of configurations shows the advantages of using steam networks for decreasing exergy losses and CO2 emission due to the high CO2 emission of the grid electricity. However, BRC loses its interest when steam networks are integrated. The boiler has little competitive advantage as the gas turbine and gasifier combination can provide heat and electricity with lower CO2 emission. In conclusion, the developed model is able to study the effects of poly-generation technologies on environmental and economic targets by a decomposition approach. In the future study, district networks, thermal panels and photovoltaics (PV), as well as storage systems should be integrated in the optimization model.