Abstract
This paper develops a discrete operation optimization model for combined heat and powers (CHPs) in deregulated energy markets to maximize owners’ profits, where energy price forecasting is included. First, a single input and multi-output (SIMO) model for typical CHPs is established, considering the varying ratio between heat and electricity outputs at different loading levels. Then, the energy prices are forecasted with a gray forecasting model and revised in real-time based on the actual prices by using the least squares method. At last, a discrete optimization model and corresponding dynamic programming algorithm are developed to design the optimal operation strategies for CHPs in real-time. Based on the forecasted prices, the potential operating strategy which may produce the maximum profits is pre-developed. Dynamic modification is then conducted to adjust the pre-developed operating strategy after the actual prices are known. The proposed method is implemented on a 1 MW CHP on a typical day. Results show the optimized profits comply well with those derived from real-time prices after considering dynamic modification process.
Highlights
The combined heat and power (CHP) system produces heat and electricity with high efficiency by consuming oil, natural gas, and biomass, etc. [1]
A single input and multi-output (SIMO) model is established for CHP, whose overall efficiency and Heat to power ratio (HTPR) are both in variation with the loading level
The optimal operation routine is mainly dominated by high electric prices and high electric demand
Summary
The combined heat and power (CHP) system produces heat and electricity with high efficiency by consuming oil, natural gas, and biomass, etc. [1]. The overall efficiency is generally in positive correlation with the loading level of CHPs, i.e., a high loading level means high overall efficiency Another problem for existing researches is that the profit optimization of CHP considering the forecast prices is neglected, where the profits of pre-developed operating strategy differ greatly from those of adjusted strategy in response to actual prices. To optimize the profits of CHPs in real-time, there are several challenges: (i) to determine the heat and electricity output proportion, i.e., HTPR; (ii) to forecast energy prices; (iii) to adjust pre-developed operating strategies to comply with actual conditions. Energies 2015, 8, 14330–14345 optimization model is developed to obtain the optimal operating points, which may produce Emnearxgiiems 2u0m15, p8,rpoafigtes–.paTghee profits are optimized every 30 min, which determine the heat and electricity output in real-time. TThhee rreemmaaiinniinngg ppaarrttss ooffththisisppaappeerraraereorogragnainziezdedasasfoflolollwows.sI.n ISnecStieocntio2n, t2h,e tShIeMSOIMmOodmelodfoerl CfoHr PCHisPesitsabeslitsahbeldis,hwedh,owsehoovseeroavlleerfaflilcieefnficcyieanncyd aHnTdPHR TbPoRthbvoatrhyvwairtyh wthiethlothadeinlogadleivnegl.leTvheel.prTihcee fporriecceafsotrinecgasmtientghomdetihsopdreissepnrteesdenitnedSienctSioecnti3o.nT3h. eTnhethnethdeisdcirsectreetoepotpimtimizaiztaiotinonmmodoedlelfoforrCCHHPP iiss ddeevveellooppeedd iinn SSeeccttiioonn44, ,foflollolwowededbybythtehdeydnyanmaimc picropgrroagmramminmginaglgoarlgitohrmith. mIn.SIenctSioenct5io, na 15,MaW1 CMHWP CisHoPptiismoipzetidmwiziethd twhiethprtohpeopsreodpaopsepdroaapcphr. oFaicnha.llFyi,ncaolnlyc,lucsoinocnlsusairoendsraarwendrinawSencitnioSne6c.tion 6
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