Complicated Energy Systems Research Articles

Optimal trading strategies for multi-energy microgrid cluster considering demand response under different trading modes: A comparison study

The linkage of neighboring multi-energy microgrids may overcome the disadvantage of one single microgrid since the surplus/deficient energy can meet internal balance and realize self-satisfaction. In order to realize successful cooperation of such a complicated energy system, optimal trading strategy and reasonable trading price among all entities are important. In this study, a bi-level optimization model combined of two trading modes is proposed under different scenarios: an intermediary agent-based trading mode where the intermediary agent acts as an intermediate operator, as well as a direct trading mode without intermediary agent. The Stackelberg game theory-based method and the supply/demand ratio method are used for price determination, respectively. In addition, the integrated demand response is also considered in this study. As an illustrative example, a comparative study is implemented in a neighboring energy cluster combing three park-level multi-energy microgrids. The results show that, compared with the situation that each multi-energy microgrid acts alone, a cluster obviously increases total benefits, which may contribute to the development of tailored policy instruments. In addition, although direct trading among multi-energy microgrids enjoys higher benefits, its self-sufficient ability is lower than the situation considering intermediary agent, which may help policymakers to predict the merits and demerits associated with different energy trading modes among multi-energy microgrids in a more accurate way.

Optimization and design of heat recovery system for aviation

Heat recovery remains an active topic in energy system research because of the potential benefits in energy consumption and energy efficiency. Among the recent advances, the intercooled recuperated heat recovery concept has provided significant advancements in energy conversion for gas turbine engines. However, when applying this concept to aero engines, the lack of available space in the engine and the low tolerance for pressure loss are critical challenges in this complicated energy system. Therefore, an improved structure for a complex heat recovery energy system called the intercooled recuperated turbofan engine is proposed. In addition, a novel systematic optimization design method based on multidisciplinary theories is introduced. The new heat recovery system design is designed and analyzed with respect to the installation space and the engine performance. Because the system is very complex and strongly coupled, a sensitivity analysis has been conducted to decouple the parameters of this non-linear problem. By optimization, the structure of the engine is modified to adapt the heat recovery system′s demand for space (through a change in air flow). Its energy saving benefits are compared to a traditional turbofan engine; the new, optimized design has a lower fuel consumption. It achieves a high overall efficiency with a lower overall pressure ratio. This systematic design method is extremely important for complicated energy systems: it addresses the necessary compromises between structure and performance, which remains a critical challenge for energy systems.

Analysis of emission taxes levying on regional electric power structure adjustment with an inexact optimization model - A case study of Zibo, China

In this study, an inexact two-stage chance-constrained programming (ITSCCP) model was provided for multiple electrical power system supply and demand management in Zibo City under uncertainties. Three scenarios about the electric power structure adjustment, renewable power generation, and the emission taxes were designed. Methods of two-stage stochastic programming (TSP) and inexact chance-constrained programming (ICCP) were incorporated into the developed model to tackle uncertainties in terms of various cost coefficients, decision maker's risk attitude which was described by interval values and probability distributions. Moreover, under the objective of cost minimize, the electrical power generation planning for each terms under different feasibility degrees (violating constraints or available resources situations) can be obtained. The results indicated that higher probability of violating system constraints would increase risk of system, but lower the total cost; the proportion of optimized thermal power generation and imported electricity would decrease, which could promote the energy conservation and emissions reduction in some degree. At the same time, the model results are valuable for decision-makers to tackle the uncertainty of the power generation schemes within a complicated energy system and make a desired compromise between the satisfaction degree of the economic benefits and feasibility degree of constraints.

Application of the Thermodynamic Cycle to Assess the Energy Efficiency of Amine-Based Absorption of Carbon Capture

The thermodynamic cycle, as a significant tool derived from equilibrium, could provide a reasonable and rapid energy profile of complicated energy systems. Such a function could strongly promote an in-depth and direct understanding of the energy conversion mechanism of cutting-edge industrial systems, e.g., carbon capture system (CCS) However, such applications of thermodynamics theory have not been widely accepted in the carbon capture sector, which may be one of the reasons why intensive energy consumption still obstructs large-scale commercialization of CCS. In this paper, a kind of thermodynamic cycle was developed as a tool to estimate the lowest regeneration heat (Qre) of a benchmark solvent (MEA) under typical conditions. Moreover, COPCO2, a new assessment indicator, was proposed firstly for energy-efficiency performance analysis of such a kind of CCS system. In addition to regeneration heat and second-law efficiency (η2nd), the developed COPCO2 was also integrated into the existing performance analysis framework, to assess the energy efficiency of an amine-based absorption system. Through variable parameter analysis, the higher CO2 concentration of the flue gas, the higher COPCO2, up to 2.80 in 16 vt% and the Qre was 2.82 GJ/t, when Rdes = 1 and ΔTheat-ex = 10 K. The η2nd was no more than 30% and decreased with the rise of the desorption temperature, which indicates the great potential of improvements of the energy efficiency.

Management of household electricity consumption under price-based demand response scheme

The increasing electricity demand in the residential sector creates growing pressure on energy supply. The price-based demand response has been considered the most effective scheme to match supply and demand in residential sector. This paper established a multi-agent system framework to simulate the various types of energy demands in a multi-occupant household under the price-based demand response scheme. The results showed that the total electricity consumption and related costs could be reduced by 7% and 34%, which amount to 3.42 kWh and 4.63 RMB, without interruption to the household indoor comfort or degradation of their living quality. The different levels of electricity price sensitivity are responsible for 1.97 kWh electricity consumption curtailment and 4.30 RMB cost curtailment difference in a single day. Among the various types of loads, the shiftable loads have the largest price-based demand response potential, while the biggest contribution to energy saving is made by the sheddable loads. Cost savings are mainly delivered by the shiftable loads, followed by the sheddable loads and on-demand loads. In addition, EVs represent huge potential of load shifting and a large pool of energy storage, given the availability of the technique vehicle to grid. The multi-agent system model provides a generic framework for planning, simulating and optimizing complicated energy systems, which could help policy makers, power generators and utility managers to effectively manage the energy consumption in urban cities.

Decomposing core energy factor structure of U.S. residential buildings through principal component analysis with variable clustering on high-dimensional mixed data

Numerous energy computing frameworks were created with the aim of sustaining energy efficiency strategy to achieve residential sustainability in the U.S. While beneficial, without generic information on factor structure within building energy systems, most extant instruments are inclined to scope explanatory factor variables subjectively and diversely. Consequently, their intended utility often decreases with potential unstableness and limited generalizability among complicated energy system interactions. To overcome these issues, this paper develops a novel systematic homogeneity decomposition approach combining variable clustering and principal component analysis to identify key energy factor structure of residential buildings at the U.S. nation level. This study quantitatively results that, 32 key inter-heterogeneous energy variables (mean variance inflation factor=1.21) appear sufficient to robustly profile the U.S. residential systems with an average Pearson correlation of 0.86 while reducing data burden by 68%. Top three significant variables relate to heating degree days, indoor environment and building vintage, respectively explaining 13%, 11% and 9% of energy variations. Thus, two major contributions are expected as follows. (1) These above obtained quantitative results can provide objective information for decision makers to sensibly select critical variables for robust energy computing with improved interpretability and generalizability by commonly using the above simplified 32-factor space (extracted from a 99-factor space) while saving data cost. (2) The developed novel approach can be useful in other countries for energy factor structure decomposing purpose since it has no geographical restrictions.

Adapted computational method of energy level and energy quality evolution for combined cooling, heating and power systems with energy storage units

Current energy quality evaluation indexes mainly focus on depicting energy quality in systems whose character temperature and pressure are higher than environment. However, when the temperature and the pressure are lower than environment, these indexes sometimes fail to properly assess the energy quality of the system. Meanwhile, quoting different references or benchmarks in the calculation of some thermodynamics quantities, one of which is energy quality, may also confuse energy system designers. These situations will thereby result in potentially incomplete or partial evaluation of energy quality in complicated energy systems like combined cooling, heating and power (CCHP) systems. In this research, an adapted computational method of energy level which describes energy quality from thermal and mechanical perspectives has been proposed. In this method, energy quality of all-temperature and all-pressure conditions can be quantified without quoting different benchmarks, which allows thorough energy quality analysis for complicated energy systems. Energy quality evolution of a CCHP system with energy storage units (ESUs) has been discussed in terms of the proposed energy levels. The thermal energy quality evolution depicted by thermal energy level indicates that high energy level from waste heat of power generation units will cause large energy quality loss in the absorption refrigerator (AR), since the generation temperature in the AR confines the energy level in the generator to a comparatively low value. In the CCHP power generation unit, thermoacoustic engines prevail over gas turbines in energy quality conservation. Furthermore, mechanical energy level analysis shows that the absorption refrigerator and the absorption cooling storage units have higher mechanical energy levels than other components in CCHP systems due to the high vacuum conditions. Finally, an energy level matching map is provided to suggest matching strategies between waste heat sources and energy storage techniques in CCHP systems.

Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China

In this study, a multistage inexact stochastic robust model was developed for regional energy system management in Jining City, China. Three scenarios about the electric power structure adjustment, clean power generation, and the emission reduction target are designed. Methods of interval parameter programming (IPP), stochastic robust optimization (SRO), and multistage stochastic programming (MSP) were incorporated into the developed model to tackle uncertainties described by interval values and probability distributions. The results indicated that the model can provide an effective linkage between conflicting economic cost and the system stability, and different power demand levels correspond to different electricity generation schemes with varied system cost and system-failure risk. The net system cost, power generation schemes, exported electricity, and captured CO2 amount were analyzed. The results indicated that the power generation by traditional technology would be decreased with the improvement of regional energy structure adjustment and environmental protection request. The modeling results are valuable for supporting the adjustment or justification of the existing power generation schemes within a complicated energy system under uncertainty.

Simultaneous Integrated stochastic electrical and thermal energy expansion planning

In this study, a stochastic multi-objective framework is proposed for energy expansion planning (EEP). The proposed multiobjective framework can concurrently optimise the competing objective functions including total real energy losses, voltage deviation and the total cost of the installation equipments. Also, regarding the uncertainties of the new complicated energy systems, in this study, for the first time, system uncertainties including load uncertainty are explicitly considered in the EEP problem by the use of the probabilistic load flow technique based on the point estimate method. Since the objectives are different and incommensurable, it is difficult to solve the problem by the conventional approaches that may optimise a single objective. Hence, the metaheuristic algorithm is applied to this problem. Here, the particle swarm optimisation (PSO) algorithm as a new evolutionary optimisation algorithm is utilised. To improve the total ability of the PSO for global search and exploration, a new modification adaptive process is suggested in such a way that the algorithm will search the total search space globally. To evaluate the feasibility and the effectiveness of the proposed algorithm, three modified standard distribution systems are used as the case studies.

Energy expansion planning by considering electrical and thermal expansion simultaneously

This study focused on the expansion planning optimization of energy systems employing two forms of energy: the expansion of electrical and thermal energies simultaneously. The main objective of this investigation is confirming network adequacy by adding new equipment to the network, over a given planning horizon. The main objective of the energy expansion planning (EEP) is to minimize the real energy loss, voltage deviation and the total cost of installation equipments. Since the objectives are different and incommensurable, it is difficult to solve the problem by the conventional approaches that may optimize a single objective. So, the meta-heuristic algorithm is applied to this problem. Here, Honey Bee Mating Optimization algorithm (HBMO) as a new evolutionary optimization algorithm is utilized. In order to improve the total ability of HBMO for the global search and exploration, a new modification process is suggested such a way that the algorithm will search the total search space globally. Also, regarding the uncertainties of the new complicated energy systems, in this paper for the first time, the EEP problem is investigated in a stochastic environment by the use of probabilistic load flow technique based on Point Estimate Method (PEM). In order to evaluate the feasibility and effectiveness of the proposed algorithm, two modified test systems are used as case studies.

Numerical Simulation of Convective Heat Transfer Modes in a Rectangular Area With a Heat Source and Conducting Walls

Laminar conjugate heat transfer in a rectangular area having finite thickness heat-conducting walls at local heating has been analyzed numerically. The heat source located on the left wall is kept at constant temperature during the whole process. Conjugate heat transfer is complicated by the forced flow. The governing unsteady, two-dimensional flow and energy equations for the gas cavity and unsteady heat conduction equation for solid walls, written in dimensionless form, have been solved using implicit finite-difference method. The solution has been obtained in terms of the stream function and the vorticity vector. The effects of the Grashof number Gr, the Reynolds number Re, and the dimensionless time on the flow structure and heat transfer characteristics have been investigated in detail. Results have been obtained for the following parameters: 103≤Gr≤107, 100≤Re≤1000, and Pr=0.7. Typical distributions of thermohydrodynamic parameters describing features of investigated process have been received. Interference of convective flows (forced, natural, and mixed modes) in the presence of conducting solid walls has been analyzed. The increase in Gr is determined to lead to both the intensification of the convective flow caused by the presence of the heat source and the blocking of the forced flow nearby the upper wall. The nonmonotomic variations in the average Nusselt number with Gr for solid-fluid interfaces have been obtained. The increase in Re is shown to lead to cooling of the gas cavity caused by the forced flow. Evolution of analyzed process at time variation has been displayed. The diagram of the heat convection modes depending on the Grashof and Reynolds numbers has been obtained. The analysis of heat convection modes in a typical subsystem of the electronic equipment is oriented not only toward applied development in microelectronics, but also it can be considered as test database at creation of numerical codes of convective heat transfer simulation in complicated energy systems. Comparison of the obtained results can be made by means of both streamlines and temperature fields at different values of the Grashof number and Reynolds number, and the average Nusselt numbers at solid-fluid interfaces.

Increasing power plant efficiency by fuel drying

The process of pre-drying of fossil fuels — in this case raw lignite with a water content of 35 to 55 w% — leads to an increase, of the overall efficiency by 2 to 3 %points achieved by a reduction of primary fuel. The overall efficiency can be increased by up to 5%points by integration of a vapor condenser and energy reintegration. The necessary heat for the drying process is taken from the process cycle in the form of low pressure steam. Economic reasons limit the rest water content of the dried coal between 10 and 12 w%. The present paper discusses the use of process simulation programs for the thermodynamic optimization of the performance of complicated energy systems and presents six variants for increasing power plant efficiency by lignite drying.