. Optimal investment strategies for district heating plants under prediction uncertainties for cost parameters th International PhD Day of the AAEE Student Chapter Nikolaus Rab Energy Economics Group (TU Wien) Vienna, March , . Overview Overview / . Overview An investment model for district heating generation is developed that particularly concerns prediction uncertainties of cost parameters. It will be based on three different methods: . Generation expansion planning. For cost-optimal power plant investments a linear program is well-established in energy economics. . CHP planning. A simple model for short-term CHP planning is described that will be used to adapt the standard GEP formulation for district heating investments. . Risk measures for prediction uncertainties. The impact of prediction uncertainties on the heat generation costs will be quantified by risk measures that are widely used in financial mathematics. Overview / . Methodolgy Methodolgy / . I. Generation expansion planning . Hourly generation decisions: Minimizing the hourly generation costs Vi,h (in Euro/MWh) of the ith power plant with generation gi,h MWh. Ot := min gi,h Methodolgy ∑ Vi,h gi,h . i,h / . I. Generation expansion planning . Hourly generation decisions: Minimizing the hourly generation costs Vi,h (in Euro/MWh) of the ith power plant with generation gi,h MWh. Ot := min ∑ gi,h Vi,h gi,h . i,h . Yearly investment decisions: Minimizing the yearly fixed costs Fi,t in Euro/kW with binary investment decisions ni,t while taking operation costs Ot (ni,t ) into account: min ni,t Methodolgy t ∑∑ Fi,t ni,s + Ot (ni,t ) i,t s=1 / . II. CHP planning The feasible operation region of a CHP plant with power generation Gel and heat generation Gh is modelled as a convex polygon with extreme points (h1 , p1 ), (h2 , p2 ), . . . . Gel Gel (h4 , p4 ) (h3 , p3 ) (h1 , p1 ) (h2 , p2 ) . Any point in the feasible operation region can be displayed as a convex combination of those four extreme points j = 1, . . . , 4 resulting in the minimum variable costs: ∑∑ min λi,j vi,j st . . . λi,j Methodolgy i Gh Gh j∈Ji / . III. Risk measures For measuring the impact of prediction uncertainties of the cost parameters (gas, electricity, CO emissions, ...) on the distribution of the overall heat generation costs risk measures are used. Most common examples include: . Volatility risk measure (µ + σ): classic dispersion based risk measure. . Value-at-risk (Varα ): maximum costs for heat generation in the best α% of all possible cases. . Expected shortfall (ESβ ): average costs for heat generation in the worst β% of all possible cases. Methodolgy / . Optimization program (simplified) . Hourly generation decisions: The hourly generation costs Oh (ni,t , ω) depend on ω ∈ Ω and are therefore random variables. Oh (ni,t , ω) := min λ Methodolgy ∑∑( ) Vi,h,j (ω) − gel (ω) s (ω) λi,h,j (ω) h i,h,j i∈I j∈Ji / . Optimization program (simplified) . Hourly generation decisions: The hourly generation costs Oh (ni,t , ω) depend on ω ∈ Ω and are therefore random variables. Oh (ni,t , ω) := min λ ∑∑( ) Vi,h,j (ω) − gel (ω) s (ω) λi,h,j (ω) h i,h,j i∈I j∈Ji . Yearly investment decisions: A risk measure R(·) is used to include stochasticity of the hourly generation ∑ costs h Oh (ni,t , ω) into the investment decisions. min := n Methodolgy t ∑∑∑ t∈t i∈I s=1 Fi,t ni,s + R ∑∑ Oh (ni,t , ω) , t∈T h∈Ht / . Prediction uncertainties Yearly characteristics (distribution parameters) of all cost data and the district heating load can be given as a stochastic or deterministic process. During the numerical evaluation hourly synthetic profiles are generated based on ARMA-GARCH modelling for the corresponding yearly distributional parameters. 55 Price [in Euro/MWh] 30 35 40 45 Correlation 0.4 0.6 0.03 Density 0.02 20 30 40 50 Price [in Euro/MWh] 60 20 0.0 10 25 0.2 0.01 0.00 0 Daily seasonal pattern 50 1.0 Auto correlation 0.8 0.04 Distribution 5 Lags 10 15 Time [in hours] 20 Time series characteristics and distribution of the spot electricity price. Methodolgy / . Results Results / . 100 MW Electricity 200 300 400 500 600 Case study portfolio 0 CHP 1 CHP 2 0 50 100 150 MW Heat 200 250 300 Feasible operation regions of the two CHP plants. Results The case study portfolio comprises two CHP plants with a maximum heat generation of and MW (range of η el of . % to % and . % to %). For peak loads a gas fired district heating boiler with a maximum capacity of MW and a minimum capacity of . MW is installed (thermal efficiency η th of % to %). / . 0.010 Case study load 0.008 Density 0.004 0.006 0.002 0.000 The average load for district heating will be on warm days with a temperature above °C at MW, on cold days with a temperature below °C at MW. Peak loads will be up to MW. Temperature > 15°C (67%) Temperature < 15°C (33%) 0 100 200 300 District heating load [in MW] 400 Density conditional on the outside temperature for the corresponding district heating load. Results / . Optimization Possible additional technologies include heat pumps ( MW each), electric boilers and solar district heating. For the analysis the development of some cost parameters is uncertain for two different cases: . Case : The gas price has an uncertain development with its mean remaining at Euro/MWh with a standard deviation of one Euro/MWh. The spot electricity price rises up to Euro/MWh in mean (no uncertainties). . Case : In addition to case also the development of the mean spot electricity price is uncertain with a standard deviation of one Euro/MWh. Results / . Existing generation portfolio 400 Existing portfolio 0 50 100 150 MW 200 250 300 350 Heat plant CHP 2 CHP 1 0 1000 2000 3000 4000 5000 Time [in hours] 6000 7000 8000 Shares of districting heating supply for the existing generation portfolio for one reference year. Results / . ... and portfolio a er optimization. 400 Existing portfolio + 100 MW heat pumps 0 50 100 150 MW 200 250 300 350 Heat pumps Heat plant CHP 2 0 1000 2000 3000 4000 5000 Time [in hours] 6000 7000 8000 Shares of districting heating supply for the optimized portfolio with MW additionally installed heat pumps (optimal for both cases). Results / . Heat generation costs (Case ) 0.12 Case 1 0.00 0.02 0.04 Density 0.06 0.08 0.10 Existing Portfolio + 100 MW heat pumps 20 25 30 35 Heat generation costs [in Euro/MWh] Existing portfolio + MW heat pumps Results µ . . µ+σ . . 40 VaR . . % ES % . . / . Heat generation costs (Case ) 0.12 Case 2 0.00 0.02 0.04 Density 0.06 0.08 0.10 Existing Portfolio + 100 MW heat pumps 20 25 30 35 Heat generation costs [in Euro/MWh] Existing portfolio + MW heat pumps Results µ . . µ+σ . . 40 VaR . . % ES . . % / . Conclusion Conclusion / . Concluding remarks . Uncertain developments of several costs parameters have a strong influence on the composition of the optimal district heating generation portfolio. Conclusion / . Concluding remarks . Uncertain developments of several costs parameters have a strong influence on the composition of the optimal district heating generation portfolio. . Using only deterministic scenarios neglects the uncertainty nature of the GEP problem. The resulting scenario specific portfolios aren’t very useful for investment decisions. Conclusion / . Concluding remarks . Uncertain developments of several costs parameters have a strong influence on the composition of the optimal district heating generation portfolio. . Using only deterministic scenarios neglects the uncertainty nature of the GEP problem. The resulting scenario specific portfolios aren’t very useful for investment decisions. . Prediction uncertainties of cost parameters influence the expected heat generation costs as well as the risk exposure towards cost parameter changes. Risk measures (in particular value-at-risk and expected shortfall) allow for a suitable consideration of these down-side risks. Conclusion / Contact: . Conclusion. Nikolaus Rab [email protected] Institut für Energiesysteme und Elektrische Antriebe Energy Economics Group eeg.tuwien.ac.at
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