Optimal Allocation of renewable Energy Parks: A Two Stage Optimization Model. Mohammad Atef, Carmen Gervet German University in Cairo, EGYPT

Transcription

1 Optimal Allocation of renewable Energy Parks: A Two Stage Optimization Model Mohammad Atef, Carmen Gervet German University in Cairo, EGYPT JFPC

2 Overview Egypt & Renewable Energy Prospects Case study & prototype Results Next steps 2

3 Context Egypt supports the creation of renewable energy parks Investments from Germany, Japan, Danemark, Near future targets Cover 20% of Electricity demand by Renewable by 2020 Strategic planning is essential Questions from private & governmental investors When shall we invest in potential parks today/tomorrow? Solar or wind energy? Both? How much to invest? 3

4 Egypt: Wind Potential Cheap(est) RE today Fluctuates with seasons Maintenance cost is high Wind Atlas of Egypt [NREA] 4

5 Egypt: Solar Potential Expensive (today) Very stable Can be combined with Desalination Water treatment Average solar radiation in March (NREA) 5

6 Techno-Economic Challenges Technical aspects Where should a park be built Terrain & grid connection Environment potentials & resource to consider Technology (turbines, CSP, PV) Economical aspects Timing of the investment Cost of RE source Quantitative aspects Our Goal: plan for short and longer term Help decision maker & domain expert answer these questions by building an optimization tool 6

7 Project Funded by STDF Science and Technology Development Fund (Egypt) Approach Develop prototype decision support tool for optimal park placement Investigate different models and algorithms Seek real data to simulate with Current results Running prototype tool Won 1 st DESERTEC thesis award in Fall 2011 (Master Student) 7

8 Core Problem Statement Given Wind & solar Atlas of Egypt Electricity demand per month Set of potential wind and solar park locations Find Total cost (including installation, maintenance, connection) Energy yield per potential park mwh Optimal subset of potential parks such that 20% of demand per month is covered at minimal cost 8

9 Intuition: satisfy investors & energy needs Playing tetris + taking into account cost & physical constraints 9

10 Our model: Input data (1) Digitalize data from the atlas purchased Extract wind power/direction Extract solar radiations on the map (avr per month) 10

11 Our model: constraints & objectives (2) Consider a set of potential parks (B i ) Location, Energy gain kwh G ij Total cost (installation, maintenance, connection) per park C i 20% of electricity demand must be satisfied by RE parks per month D j The total cost must be minimal 11

12 Methods & algorithms evaluated Dynamic programming Complete and optimal but pseudo-polynomial Constraint-based local search Efficient but no guarantee of optimality Integer Linear programming Suitable given constraints types & objective (linear) Efficiency not guaranteed in general for 0-1 ILP problems 12

13 Graphical interface: Parks initialization 13

14 Dynamic Programming Approach (1) Our model is equivalent to multi-dimensional knapsack problem Solvable using DP techniques Intuition: recursive formulation - Optimal substructure If a park is picked then it works full power Remaining demand to cover = demand park coverage New cost to pay = old cost + added park cost If park not picked Demand to recover remains the same Cost to pay is old cost 14

15 Dynamic Programming Approach (2) Minimize cost function F(n,d) n: nb of parks d: demand vector Recursive formulation Preprocessing Ordering of the parks by highest energy gain first 15

16 Constrained Local Search Idea Improve time complexity, but loose potentially optimality Neighborhood operator E.g.: Y=[1,2,3,4] Neighborhood(Y)={[2,1,3,4],[3,2,1,4],[4,2,3,1], } But [3,1,2,4] neighborhood(y). Greedy heuristic Try to remove each park while satisfying the demand coverage for each permutation Choose the solution that minimizes overall cost Local move Move to the best solution so far and reiterate.. 16

17 What do we have so far Dynamic programming model Optimum but costly Constrained local search Optimality not guaranteed but efficient 17

18 Integer Linear Programming Proved most effective despite integrality constraints Approach: Simplex + Branch and bound search Use default settings from CPLEX (called via ECL i PS e ) 18

19 Solution for current data set Optimal solution Techniques comparison 19

20 On larger Instances 20

21 Issue: No account for future data Leading to potentially expensive & obsolete decisions Why? S.R. Bull, Renewable energy Today and Tomorrow, IEEE vol. 89(9) 21

22 New model: embed forecast data Extended model Add forecast RE costs Add forecast electricity demand Add variables for future park installation as well Outcome Combination of parks to be built now or tomorrow 22

23 Core contribution: Two stage optimization model 23

24 Algorithm for 2 stage model 1. Run CPLEX MIP on Initial model 2. Set new constraint with upper bound on the cost 3. Run CPLEX on the extended forecast model 24

25 Comparative results Optimal solution with present data Optimal solution with forecast model 25

26 Solution quality: Looking-ahead pays off 26

27 Conclusion & future work Contribution First approach in optimal park selection problems to consider forecast data Very promising results, efficient and insightful Future work Forecast data based on growth factor Investigate stochastic models & interval data constraint models Acquire more fine tuned cost figures 27

28 Thank you! Questions? 28

29 Concept of Contract Project carried out in UK in 1999: Privatization of Scottish energy market 29

30 Decision Making under Uncertainty Uncertain elements Forecast costs Forecast demand Energy yield Risk management Maximize return at minimal cost Multi-criteria optimization problem under uncertainty 30

31 Focus on Egypt: Energy consumption 31

32 Mathematical Models 2 parallel approaches Stochastic multi-stage optimization Interval data constraint model Linear models with uncertain parameters Current status Problem specification & conceptual model Physical and financial constraints Prototype GUI 32

33 Our approach Proof of concept Bachelor thesis 2009 (Yahya Mowiena) Real data from the NREA (New and Renewable Energy Authority) Constrain optimization model Model and methods GUI to interact and visualize results 33