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Abstract In Concentrated Solar Power Plants, steam turbines controlled with standard Proportional Integrative Derivative (PID) methods may suffer from performance downgrading in power generation when the steam conditions deviate from nominal ones. An enhancement of standard steam turbine controller can be the key to achieve optimal performance also in non-nominal steam conditions. This paper presents the improvement of the PID control concept by exploiting Fuzzy Logic, an artificial intelligence technique that allows taking into account the human experience and knowledge on the system behavior. A real Concentrated Solar Power Plant has been modeled focusing on generated power control loop, its stability and performance analysis, knowledge useful to design a Fuzzy Inference System. A fuzzy logic controller is proposed to continuously adapt the PID parameters, to improve the steam turbine governor action. Its performance is compared to the classical PID tuned according to three different approaches. The fuzzy logic PID controller extends the simplicity of PID and adapts the control action to actual operating condition by providing the system with a sort of “decision-making skill”. The possibility to design implementable algorithms on a Programmable Logic Controller, which have stringent computational speed and memory requirements, has been explicitly taken into account in the developed work, through the minimization of the controller complexity with a reduced number of fuzzy sets and fuzzy rules within the fuzzy inference system.

Abstract Temperature is one of the factors which affect the power availability, driveability and durability of the battery pack. Folded fin and serpentine channel are commonly used to provide cooling for the battery pack. During the cooling process, fluid absorbed the heat generated along the flow direction and caused the reduction of the cooling capacity. Hence, downstream temperature is always higher than the upstream temperature. Inconsistent cooling effect will lead to high variation of temperature distribution and shorten the life expectancy of the battery pack. In this study, a battery module consists of three pieces of LiFePO4 pouch cell arranged side by side, and aluminium foam is sandwiched between two heat spreaders to form a cooling plate. Aluminium foams with different porosity and pores density were modelled to investigate the thermal performance and flow field numerically. Correlation of Nusselt number, permeability and resistance loss coefficient from the literature was extracted and used in the CFD simulation. From the simulation results, it is shown that 10 PPI aluminium foam with 0.918 porosity offered the highest thermal performance and lowest flow resistance. Hence, the optimized aluminium foam cooling plate can be used as a new type of cooling system for the battery pack.

Abstract Computable general equilibrium models are becoming popular for simulating emissions trading systems. However, these models make various assumptions about the production structure and the possibilities of substituting energy for productive factors or inputs. Therefore, this study aims to analyze how different options to incorporate energy into the nested production structure of a computable general equilibrium model affect the obtained impacts when an emissions trading system is implemented. A flexible computable general equilibrium model called ECOMODEL is developed, which is the first model that allows choosing any of the three nested production structures most used in the literature (KEL-M, KL-EM, or KLE-M). The Chilean economy is used as a case study since there is a current database with high disaggregation of the energy sector for calibrating the computable general equilibrium model. The results show that the simulated impacts with KEL-M and KL-EM structures are the best when elasticities of substitution equal to the values ​​most frequently used in previous studies are chosen. However, the KEL-M structure that considers energy as a substitute for capital provides overly optimistic environmental results when high elasticities of substitution are used, obtaining the lowest reduction in Gross Domestic Product and the lowest price of the emissions trading system. Furthermore, the KLE-M structure gives unrealistic results regardless of the elasticities of substitution used. In consequence, a KL-EM nested production structure should be prioritized to simulate an emissions trading system since it provides realistic results and is less sensitive to the values of the elasticities of substitution.

Abstract The urge to increase renewable energy penetration into the power supply mix has been frequently highlighted in response to climate change. South Korea was analyzed as a case study for which the government has shown motivation to increase renewable energy penetration. Herein, a hybrid renewable energy system (HRES) including solar and wind energies were selected due to their relatively stable and mature technology. In addition, Power-to-X has been incorporated to cover other renewable energy options such as hydrogen and synthetic natural gas (SNG). Therefore, an approach of forecasting the weather characteristics and demand loading over a relatively long timeframe was implemented via deep learning techniques (LSTM and GRU) and statistical approaches (Fbprophet and SARIMA), respectively. A deployment strategy incorporating HRES and Power-to-X is then proposed in correspondence to the forecasted results of the 15 regions considered in this study. An extension of this, the reliability of the designed system is further assessed based on the probability of the demand losses with the aid of Monte-Carlo simulation. With the proposed deployment strategy, a total annual cost of 9.88 × 1011 $/year and a greenhouse gas reduction of 1.24 × 106 tons/year are expected for a 35% renewable energy penetration. However, only SNG shows relatively competitive cost (at 23.20 $/m3 SNG), whereas the average costs of electricity (0.133 $/kWh) and hydrogen (7.784 $/kg H2) across the regions are yet to be competitive compared to the current market prices. Nonetheless, the priority of deployment across regions has been identified via TOPSIS.

Abstract Performance characteristics due to fouling in a vapor compression cycle with integrated mechanical sub-cooling are investigated for various applications. Considering the first set of refrigerants i.e. R134a, R410A and R407C, from a first law standpoint, the COP indicates that R134a always performs better unless only the evaporator is being fouled. From a second law standpoint, the second-law efficiency indicates that R134a performs the best in all cases. Considering the second set of refrigerants i.e. R717, R404A and R290, the COP indicates that R717 always performs better unless only the evaporator is being fouled; however, the second-law efficiency indicates that R717 performs the best in all cases. Furthermore, the performance degradation due to fouling of the evaporator always has a larger effect on cooling load capacity while the performance degradation of the condenser always has an overall larger effect on the sub-cooler compressor power requirement as well as the COP of the system. In light of the data generated, a new performance degradation law, presented earlier, is shown to be applicable for this system as well that can reduce the amount of experimentation and help predict relevant quantities of the refrigeration system.

Abstract To transform the original centralized scheduling framework of integrated community energy systems (ICES), a decentralized scheduling method is proposed for the optimal operation of ICES. Firstly, linearized mathematical models of the electric distribution system and natural gas distribution system are established to eliminate the nonconvexity and nonlinearity of the multi-energy systems. Meanwhile, an improved energy hub model is proposed by introducing the state variable, which is able to obtain the optimal solution with better accuracy and higher efficiency. Secondly, a decentralized scheduling framework is developed via an improved consensus-based alternating direction method of multipliers (ADMM). By introducing several coupling links as consensus variables, the optimal operation of the ICES is decoupled according to the physical interactions among each sub-system, in which the original model can be solved parallelly in a decentralized manner. Only the consensus variables are exchanged among sub-systems so that the privacy and confidential items of each sub-system operated by different entities can be ensured. Finally, two different ICES test cases with different system scales are utilized to demonstrate the effectiveness of the proposed method, which is able to provide scheduling scheme same as the centralized method. Dynamic step size modification is further considered in the consensus-based ADMM. By updating the step size during each iteration, the computation expense of optimization process is reduced with less consuming time, iteration and apparent oscillation, by which the convergence performance of ADMM is improved with higher computation efficiency.

Abstract This paper attempts to provide some theoretical insights into OPEC behaviour by: (1) comparing the OPEC's optimum pricing policy derived from a welfare maximisation model with that of the traditional profit maximisation model; and (2) introducing a coalition formation model which is utilised to analyse the compromised pricing policy of OPEC, while each of its members takes a different attitude regarding this matter.