Heat Storage Facilities Research Articles

계통한계가격 예측모델에 근거한 통합 지역난방 시스템의 최적화

In this paper we performed evaluation of the economics of a district heating system (DHS) consisting of energy suppliers and consumers, heat generation and storage facilities and power transmission lines in the capital region, as well as identification of optimal operating conditions. The optimization problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall operating cost of DHS while satisfying heat demand during 1 week and operating limits on DHS facilities. This paper also propose a new forecasting model of the system marginal price (SMP) using past data on power supply and demand as well as past cost data. In the optimization, both the forecasted SMP and actual SMP are used and the results are analyzed. The salient feature of the proposed approach is that it exhibits excellent predicting performance to give improved energy efficiency in the integrated DHS.

10.2478/s11814-009-0159-x

This paper presents a scheme to achieve structural and operational optimization for the heat plant in a district energy system. A district energy system consists of energy suppliers and consumers, district heating pipelines and heat storage facilities in a region. Production and consumption of energy and transport of energy as well as storage of heat are taken into account in the model. The problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall cost of the district energy system. Evaluation of the energy production cost is based on the daily operation for every season at the plant located at Suseo in Seoul, Korea. From the results of numerical simulations we can see that the district energy system is well approximated by the proposed model, and that the energy efficiency is improved by the application of the optimal operation conditions provided by the proposed model.

10.2478/s11814-009-0282-8

A district heating system (DHS) consists of energy suppliers and consumers, heat generation and storage facilities and power transmission lines in the region. DHS has taken charge of an increasingly important role as the energy cost increases recently. In this work, a model for operational optimization of the DHS in the metropolitan area is presented by incorporating forecast for demand from customers. In the model, production and demand of heat in the region of Suseo near Seoul, Korea, are taken into account as well as forecast for demand using the artificial neural network. The optimization problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall operating cost of DHS. The solution gives the optimal amount of network transmission and supply cost. The optimization system coupled with forecast capability can be effectively used as design and longterm operation guidelines for regional energy policies.

Optimal operation of the integrated district heating system with multiple regional branches

This paper presents an optimal production and distribution management for structural and operational optimization of the integrated district heating system (DHS) with multiple regional branches. A DHS consists of energy suppliers and consumers, district heating pipelines network and heat storage facilities in the covered region. In the optimal management system, production of heat and electric power, regional heat demand, electric power bidding and sales, transport and storage of heat at each regional DHS are taken into account. The optimal management system is formulated as a mixed integer linear programming (MILP) where the objectives is to minimize the overall cost of the integrated DHS while satisfying the operation constraints of heat units and networks as well as fulfilling heating demands from consumers. Piecewise linear formulation of the production cost function and stairwise formulation of the start-up cost function are used to compute nonlinear cost function approximately. Evaluation of the total overall cost is based on weekly operations at each district heat branches. Numerical simulations show the increase of energy efficiency due to the introduction of the present optimal management system.

Optimization of district heating systems based on the demand forecast in the capital region

A district heating system (DHS) consists of energy suppliers and consumers, heat generation and storage facilities and power transmission lines in the region. DHS has taken charge of an increasingly important role as the energy cost increases recently. In this work, a model for operational optimization of the DHS in the metropolitan area is presented by incorporating forecast for demand from customers. In the model, production and demand of heat in the region of Suseo near Seoul, Korea, are taken into account as well as forecast for demand using the artificial neural network. The optimization problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall operating cost of DHS. The solution gives the optimal amount of network transmission and supply cost. The optimization system coupled with forecast capability can be effectively used as design and longterm operation guidelines for regional energy policies.

Optimization of the heat plant of district energy systems

This paper presents a scheme to achieve structural and operational optimization for the heat plant in a district energy system. A district energy system consists of energy suppliers and consumers, district heating pipelines and heat storage facilities in a region. Production and consumption of energy and transport of energy as well as storage of heat are taken into account in the model. The problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall cost of the district energy system. Evaluation of the energy production cost is based on the daily operation for every season at the plant located at Suseo in Seoul, Korea. From the results of numerical simulations we can see that the district energy system is well approximated by the proposed model, and that the energy efficiency is improved by the application of the optimal operation conditions provided by the proposed model.

Numerical Analysis of Heat Storage Phenomenon in a Dual Latent Heat Sink

This paper presents the concept of a dual latent heat sink intended for low thermal duty cycle electronic heat sink applications. The effectiveness of this heat sink depends on the rapidness of the heat storage facility in the design during the pulse heat generation period of the duty cycle. Heat storage in this heat sink involves transient simultaneous laminar film condensation of vapor and melting of an encapsulated phase change material in graphite foam. The key focus of this paper is to numerically analyze this conjugate heat transfer problem including the wall inertia effect and thus verify the effectiveness of the heat storage mechanism of the heat sink. An effective heat capacity formulation is employed for modeling the phase change problem and is solved using finite element method. The results of the developed model showed that the concept is effective in preventing undue temperature rise of the heat source.

Structural and operational optimisation of distributed energy systems

A distributed energy system (DES) is a system comprising a set of energy suppliers and consumers, district heating pipelines, heat storage facilities and power transmission lines in a region. Distributed energy production has got an increasingly important role in the energy market. In this paper, a model for structural and operational optimisation of DES is presented. In the model, production and consumption of electrical power and heat, power transmissions, transport of fuels to the production plants, transport of water in the district heating pipelines and storage of heat are taken into account. The problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimise the overall cost of DES, i.e., the sum of the running costs for the included operations and the annualised investment costs of the included equipment. An illustrative example is presented for a complex DES situation. The solution gives the DES structure, i.e., which production units, heat transport lines and storages should be built as well as their locations be, together with design parameters for plants and pipelines. The model enables the involved parties—suppliers, consumers, designers and authorities—to form a joint view of different situations as a basis for the decision making. A tool based on the model is built, which can be used in design, in creating guidelines for regional energy policies and for versatile what-if analyses.

OPTIMAL DIMENSION OF A RESERVOIR FOR TEMPORARY STORAGE OF HEAT SURPLUSES FROM COMBINED HEAT AND POWER IN ARTIFICIAL LIGHTED GREENHOUSES

Producing electricity with on-site combined heat and power, to feed artificial lighting implies the production of large amounts of waste heat. Quite often the heat production exceeds the actual heat demand of the greenhouse. Thus combined heat and power induces heat surpluses. However, in a large span of time (yearround), reject heat from combined heat and power is less than the heat demand of an intensive utilized greenhouse. Thus, periods with surpluses must be considered temporary and storage of these surpluses for future use is advantageous. Another potential source of heat surpluses is the boiler, when its exhaust gases are used for carbondioxide enrichment of the greenhouse air. Carbondioxide addition is requested during the day, whereas the heat, associated with this way of CO2-production is mostly not required during the day. A buffer can store the heat produced during the day for use during the night. To determine the energy consumption and canopy growth of a greenhouse with artificial lighting, combined heat and power, CO2-enrichment of the canopy ambient and a heat storage facility a simulation model was built. In this paper the energy saving obtained by heat storage facilities with different dimensions is discussed. Comparison of the benefits with the costs related to the storage facility gives a possibility to determine an optimal storage capacity. It will be demonstrated that the amount of carbondioxide addition has a significant effect on the optimum storage capacity.

Heat pipes for heating and cooling the ground

This article reviews a wide range of heat pipe implementations and applications in the heating and cooling of soils and in underground heat storage and also conducts a comprehensive analysis of the thermal efficiency considerations of various heat pipes ranging from gravitational flow devices to those based on thermosyphons. Heat transfer and thermal conductivity calculations are presented for heat pipes working in conjunction with evaporators and condensers in a soil environment. Applications discussed include ground-based heat storage facilities for agricultural greenhouses and wintertime construction projects. Energy sources ranging from solar to biomass to fossil fuel are assessed. The use of heat pipes in solar-assisted ground source heat pumps is also evaluated.

Underground storage of heat in solar heating systems

The thermal interaction between underground heat storage and the surrounding ground is studied. The storage medium may be water in a tank, rocks, the ground itself, etc. For the case of hemispherical geometry, analytic steady-state solutions and numerical solutions to select time-dependent situations are presented and discussed. The quasi-steady-state average daily net heat loss into the surrounding ground from uninsulated underground heat-storage facilities is estimated to be only a few per cent of the heat storage capacity for typical home-size units in low-conductivity ground in the absence of ground-water flow. The economics of added insulation under these circumstances appears to be critically site and system specific. The time required for the average heat-loss rate of a new system to approach the steady-state value is on the order of a year. The ground surrounding a typical heat-storage facility contributes very little to the system's storage capacity either on a daily or a seasonal time scale.