Zoning operation of energy piles to alleviate the soil thermal imbalance of ground source heat pump systems

Energy piles have attracted increasing interest for application in ground source heat pumps,because it is environment-friendly,energy-efficient,and without additional drilling cost.However,when there is a large dif-ference between the heating and cooling loads,the system will suffer from a soil thermal imbalance which may further decline the system performance and even cause a system failure.A hybrid ground source heat pump sys-tem that integrates auxiliary equipment can solve the problem,however,it needs additional investment and a complicated control strategy.In this paper,the zoning operation of energy piles can effectively improve the tem-perature recovery ability of soil in the energy pile group and thus alleviate the soil thermal imbalance.Specifically,a heating-dominated residential building in Beijing is selected for a case study,with 144 energy piles arranged in a 12×12 layout.An analytical model of the spiral-coil energy pile group with seepage will be adopted,which can consider the groundwater flow,the geometry of spiral coils,and the thermal interaction among different energy piles,achieving high calculation accuracy and fast calculation speed.Based on this analytical energy pile model,a system model will be built to investigate the system performance influenced by different zoning operation strategies.Results show that intensive heat injection into the center of the pile group(Strategy 2 and Strategy 3)or heat extraction from the outer layer of the pile group(Strategy 4)can relieve the cold accumulation.Strategy 2 can relieve the outlet temperature decline from 5.54℃ to 4.46℃ and improve the heating COP from 3.297 to 3.423 compared to the conventional full operation strategy.Although the annual heat pump COP of Strategy 2 is a little lower than that of conventional full operation strategy,Strategy 2 has the shortest unmet heating or cooling time.Therefore,the proposed zoning operation strategy can achieve good system efficiency and excellent system reliability compared to the conventional strategy.

Modified Cuckoo Search Algorithm to Solve Economic Power Dispatch Optimization Problems

A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance.Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.

Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization

Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in revenue.Various non-linearity are added to make the fossil fuel-based power systems more practical.In order to achieve an accurate economical schedule,valve point loading effect,ramp rate constraints,and prohibited operating zones are being considered for realistic scenarios.In this paper,an improved,and modified version of conventional particle swarm optimization(PSO),called Oscillatory PSO(OPSO),is devised to provide a cheaper schedule with optimum cost.The conventional PSO is improved by deriving a mechanism enabling the particle towards the trajectories of oscillatory motion to acquire the entire search space.A set of differential equations is implemented to expose the condition for trajectory motion in oscillation.Using adaptive inertia weights,this OPSO method provides an optimized cost of generation as compared to the conventional particle swarm optimization and other new meta-heuristic approaches.

Optimal power flow calculation in AC/DC hybrid power system based on adaptive simplified human learning optimization algorithm

This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading effects of generators,carbon tax,and prohibited operating zones of generators,respectively.ASHLO algorithm,involves random learning operator,individual learning operator,social learning operator and adaptive strategies.To compare and analyze the computation performance of the ASHLO method,the proposed ASHLO method and other heuristic intelligent optimization methods are employed to solve OPF problem on the modified IEEE 30-bus and 118-bus AC/DC hybrid test system.Numerical results indicate that the ASHLO method has good convergent property and robustness.Meanwhile,the impacts of wind speeds and locations of HVDC transmission line integrated into the AC network on the OPF results are systematically analyzed.

Development and prospects of the fire space monitoring system in Kazakhstan

This article provides a brief description of the fire space monitoring system in Kazakhstan, including the GIS-technology incorporated in its structure. The system performs operative space monitoring of fire areas and burnt areas, mapping of large fire areas, analysis of seasonal and long-term dynamics of burnt areas, and estimation of fire risk zones. Examples of output information obtained from space monitoring of fires are given. Possible directions of development of fire space monitoring in Kazakhstan are specified.

Application of the invasive weed optimization algorithm to economic dispatch problems

In this paper the invasive weed optimization algorithm has been applied to a variety of economic dispatch （ED） problems. The ED problem is concerned with minimizing the fuel cost by optimally loading the electrical generators which are committed to supply a given demand. Some involve prohibited operating zones, transmission losses and valve point loading. In general, they are non-linear non-convex optimization problems which cannot be directly solved by conventional methods. In this work the invasive weed algorithm, a meta-heuristic method inspired by the proliferation of weeds, has been applied to four numerical examples and has resulted in promising solutions compared to published results.