بهینه سازی هزینه های نصب و راه اندازی فن آوری های انرژی تجدید پذیر در ساختمان: یک روش برنامه ریزی خطی

This paper demonstrates how Linear Programming (LP) can be applied to assist in the choice of renewable energy technologies for use in buildings to meet CO2 emissions reduction targets. Since there are many possibilities for combining different renewable technologies, the capital costs associated with the installation of one or more renewables can vary widely. In terms of capital investment the preferred solution will be the one at least cost, and LP provides an effective way to find this minimum through the so-called “objective function”. This project has used “lp_solve”, a free-source Mixed Integer Linear Programming solver that has been embedded in a Microsoft Excel application called Carbon emissions And Renewables for Building OPtimisation Toolkit (CARB-OPT) developed by RES Ltd in collaboration with London South Bank University (Renewable Environmental Services Ltd. (RES) is the environmental consultancy of Long and Partners Engineering Group. RES is currently involved in a Knowledge Transfer Partnerships (KTP) project in conjunction with the Faculty of Engineering, Science and the Building Environment (ESBE) at London South Bank University). This paper reports the application of this LP optimisation process for an office building case study with four alternative combinations of renewables. The process showed the technology mix that would lead to the smallest investment needed to comply with UK Building Regulations requirements and regional planning targets. In addition, the process offers a robust methodology to test the impact that the key assumptions may have upon the optimum solution.

The UK has a range of policies to meet its international and domestic targets to reduce CO2 emissions. Since almost half of CO2 emissions in the UK come from buildings, the UK Building Regulations set specific targets reductions for new buildings, which includes a 10% contribution to reductions from renewables [1]. Local Planning conditions often apply an even higher percentage, for example the reduction target set by the Greater London Authority (GLA) is (at the time of writing) 20% [2]. The required CO2 emissions reduction must be achieved by using one or a mix of renewable energy technologies (RETs). The RETs that the Greater London Authority indicates as applicable to London's developments are [3]: wind generators (W), photovoltaic systems (PVs), solar thermal water heating (ST), biomass heating (BH), biomass CHP (BCHP) and ground sources heat pumps (GSHPs). There are a number of factors that impact on the viability of RETs that need to be considered at the earliest phase of a project. These include the geometry, orientation and local overshadowing of the building (for PV and ST), the geographical position and meteorological conditions of the site (for W), the plant room and fuel storage space availability (for BH and BCHP), and the building site geological characteristics (for HP). Once a set of “feasible renewables” is determined, the next step requires an assessment of the financial viability of these technologies. Capital costs, maintenance costs and payback of RETs investment are some of the financial factors that are normally taken into consideration. Since detailed design considerations are not known at this stage it is often difficult to fix these investment costs, and yet it is at this point in the design where best value decisions need to be made. This paper presents a linear programming (LP) algorithm that can assist in minimising the RETs capital cost to achieve mandatory carbon dioxide emission reductions. The study is based on the use of the “Carbon emissions And Renewables for Building OPtimisation Toolkit” (CARB-OPT), a VBA-based user interface Excel application developed by RES Ltd in collaboration with London South Bank University. On the basis of statistical models that are drawn from the RES Ltd. energy and cost benchmark database, CARB-OPT computes (with an acceptable level of accuracy at the very first phase of a project): Part L Building Regulations carbon emissions and energy performance certificate (EPC) benchmarks, simple combined heat and power and tri-generation analysis and RET feasibility studies. With respect to the last of these, CARB-OPT employs an embedded LP calculation tool, which uses data for the feasible renewables while applying constraints that limit their application. The LP calculation tool returns a set of RET plant sizes that can meet CO2 emissions reductions requirements at least cost. The LP calculation routine takes into account a number of key parameters including the geometry of the building, the investment cost of each RET (per kW installed), the average annual meteorological conditions of the site under investigation (solar irradiation and wind speed impact on the CO2 saved per kW installed of PV, ST and W) and constraints for installation such as available area. To our knowledge, there have been no published reports on the use LP models to address the economic viability of a mix of RETs that can be installed in a building; hence the results presented here are the first innovative attempt at such an application.

In this paper we have investigated a Linear Programming application to optimise the installed costs of renewable energy technologies for buildings. In particular, a Linear Programming tool has been embedded in a Microsoft Excel application called “CARB-OPT” developed by RES Ltd in collaboration with London South Bank University. This tool processes a variety of input parameters and constraints associated to the a number of feasible renewables and returns a mix of renewable technology plant sizes required to comply with building and planning regulations at the minimum installed capital cost. The methodology has been demonstrated for a specific case study office building of 11,500 m2 located in London, and has been validated using MS Excel Solver. The results show that the investment required to achieve mandatory CO2 reductions would range between €338,122 and €2,406,403, depending on the renewables considered to be feasible. The LP process has found that the least investment (€338,122) occurred when PV, ST, W and BH were included in the LP process, with ST eventually proving to be non-viable. The exclusion of ST is supported by the fact that ST has the higher plant cost per tonne of CO2 saved each year. The paper has also presented the sensitivity analysis related to the least cost (problem P4, technology mix D3). Firstly this analysis investigated the impact that possible technology costs changes could have upon the optimised technology mix solution D3. The example showed that current cost ranges of PV would not modify the optimum technology mix. In addition an analysis has also been conducted using the concepts of “shadow prices” and “ranges of applicability” to check the variability of the least installed cost value when the constraints for each technology were changed. In this case we analysed how the least installed cost varied as a function of the emissions reduction target (€13,219/tonne per year). The sensitivity analysis applied to the external terrace space constraint showed that the external optimum space required to installing the PV and W plants is 2515 m2 and that any additional use of space would be of no benefit. We can conclude from the above that applying LP to the practical analysis of early-stage feasibility for renewables in buildings can be a powerful tool for deciding on the optimum technology mix. It can demonstrate the risks of making the wrong selection, as well as provide strong indications of the key practical constraints that impact most on a renewable energy installation.