Scheduling pnfar ar@is @isep ep.ipp. ipp.pt pt pnsfaria@gm - PowerPoint PPT Presentation

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling pnfar ar@is @isep ep.ipp. ipp.pt pt pnsfaria@gm sfaria@gmail ail.co .com Pedro Faria, João Soares, Zita Vale, Hugo Morais, Tiago Sousa GECAD – Knowledge Engineering and Decision Support Research Group Polytechnic of Porto Portugal THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES Lyngby, Copenhagen, Denmark, October 06 - 09, 2013 26-27 September 2012 | Porto – Portugal

Presentation outline  Introduction / objectives  Developed methodology  Case study  Conclusions 2 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Introduction  Objectives and motivation  Demand Response (DR) and Distributed Generation (DG) in smart grids.  Intensive use of Distributed Energy Resources (DER) and technical and contractual constraints large-scale non linear optimization problems  Particle Swarm Optimization (PSO) for a Virtual Power Player (VPP) operation costs minimization  937 bus distribution grid, 20310 consumers, 548 DG  Compare deterministic, PSO without mutation, and Evolutionary PSO. 3 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Introduction  VPP operation Customers response to DR programs Participate in the market to sell or buy energy Electricity generation based on several technologies 4 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Resources dispatch methodology Objective Function Quadratic Minimize DG costs        c X c P N  DG A DG t ( , ) DG DG t ( , ) B DG t ( , ) DG DG t ( , )     DG         2 c P P c    DG 1 C DG t ( , ) DG DG t ( , ) EAP DG t ( , ) EAP DG t ( , )     N Number  EAP SP   c P  Suppliers  of periods SP SP t ( , ) SP SP t ( , ) T      SP 1 C        DR Operation c P c P N   t 1 L  RED _ A L t ( , ) RED _ A L t ( , ) RED _ B L t ( , ) RED _ B L t ( , )     cost         c P P c   NSD  L 1 RED C L t _ ( , ) RED C _ ( , ) L t NSD L t ( , ) NSD L t ( , )     N    S     Storage charge c P c P   Dch S t ( , ) Dch S t ( , ) Ch S t ( , ) Ch S t ( , )   and discharge  S 1 Cost Power 5 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Resources dispatch methodology Balance equations i i i N N N        DG SP S      i i i i i P P P P P DG DG t ( , ) EAP DG t ( , ) SP SP t ( , ) Dch S t ( , ) Ch S t ( , )    DG 1 SP 1 S 1 Active power i N    L     balance i i i i P P P P Load L t ( , ) DR _ A L t ( , ) DR _ B L t ( , ) NSD L t ( , )  L 1   N      B         V V G cos B sin i t ( ) j t ( ) ij i t ( ) j t ( ) ij i t ( ) j t ( )  j 1         t 1,.., T ; i 1 ,.., N In each period B and each bus i i i N N N    DG SP L    i i i Q Q Q DG DG t ( , ) SP SP t ( , ) Load L t ( , )    DG 1 SP 1 L 1   N      B         Reactive V V G sin B cos i t ( ) j t ( ) ij i t ( ) j t ( ) ij i t ( ) j t ( ) power balance  j 1         t 1,.., T ; i 1,.., N B 6 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Resources dispatch methodology Constraints   min max  Bus voltage and line capacity V V V i i t ( ) i   *         max U y U U y U S        min max i t ( ) ij i t ( ) j t ( ) sh i _ j t ( ) Lk i i t ( ) i                      t 1,.., T ; i j , 1,.., N ; i j ; k 1,.., N t 1,.., T ; i 1,.., N B k B  Resources maximum capacity DR Suppliers       P X P P X P P P P DGMin DG t ( , ) DG DG t ( , ) DG DG t ( , ) DGMax DG t ( , ) DG DG t ( , ) RED _ A L t ( , ) MaxRED _ A L t ( , ) SP SP t ( , ) SPMax SP t ( , )  P P RED _ B L t ( , ) MaxRED _ B L t ( , )      Q Q Q X Q Q X  P P DGMin DG t ( , ) DG DG t ( , ) DG DG t ( , ) DGMax DG t ( , ) DG DG t ( , ) SP SP t ( , ) SPMax SP t ( , ) RED _ C L t ( , ) MaxRED _ C L t ( , )                         t 1,..., T ; DG 1,..., N t 1,..., T ; L 1,..., N t 1,..., T ; SP 1,..., N DG DG L SP  Storage constraints Zita Vale, Hugo Morais, Pedro Faria, Carlos Ramos, “Distribution System Operation Supported by Contextual Energy Resource Management Based on Intelligent SCADA”, Renewable Energy, vol. 52,pp. 143-153, April, 2013. 7 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Resources dispatch methodology - PSO  Modified PSO  Gaussian mutation  Self-parameterization  Results validation  GAMS  EPSO [Miranda, 2005]  Self-parameterization in EPSO 8 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Resources dispatch methodology - PSO  Self-parameterization  The variables with lower price have higher velocities.  1.5 maxVel 1 ( Vector of Prices ) i i generator marginal cost prices and demand response cut prices  If the energy supplier price tends to be cheaper, then the minimum velocity limits tend to be lower in order to have less load cuts. Number of variables   minVel Position in price rank 9 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Resources dispatch methodology - PSO  Mutation  Only in PSO-MUT  Particles movement            * v w v w b x w bG x     i i i i i i inertia i memory i coop  Used in each PSO iteration for diversification in the search process rather than the standard version using fixed and random weights.  Particle’s ( i ) weights ( wi ) changed in each iteration using Gaussian mutation learning parameter, externally fixed between 0 and 1 resulting particle’s      * w w N 0,1 weights after mutation i i  All the PSO solutions use an AC power flow in order to consider the network constraints and the power losses 10 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Case study – Scenarios  30 kV distribution network  60/30kV, 90MVA substation  6 feeders, 937 buses, 464 MV/LV transformers  20,310 consumers  Peak power demand is 62,630 kW  DR levels of 10% (RedA), 5% (RedB), 5% (RedC) Reduction capacity (kW) Reduction costs (m.u./kWh) Type of consumer RedA RedB RedC RedA RedB RedC Domestic 936.9 468.47 468.47 0.16 0.20 0.24 Small Commerce 798.3 399.17 399.17 0.15 0.19 0.22 Medium Commerce 1125.4 562.74 562.74 0.18 0.20 0.26 Large Commerce 1088.0 544.02 544.02 0.17 0.24 0.26 Industrial 2314.2 1157.1 1157.1 0.17 0.26 0.28 Total 6262.8 3131.5 3131.5 - - - 11 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Case study – Scenarios Price Capacity Units Resource (m.u./kWh) (kW) # PV 0.2 7061.2 208 Wind 0.05 5866.0 254 CHP 0.08 6910.1 16 Biomass 0.15 2826.5 25 MSW 0.11 53.1 7 Hydro 0.15 214.0 25 Fuel cell 0.3 2457.6 13 Supplier1 0.05 3000.0 - Supplier2 0.07 3000.0 - Price Capacity Resource (m.u./kWh) (kW) Supplier3 0.09 3000 Supplier4 0.11 3000 Supplier5 0.13 3000 Supplier6 0.15 3000 Supplier7 0.17 3000 Supplier8 0.19 3000 Supplier9 0.21 10000 Supplier10 0.23 10000 Total - 69388 12 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Case study – PSO parameters  Learning parameter = 0.8  62046 variables PSO Methodologies Parameters PSO PSO-MUT / EPSO Inertia Weight 1 Gaussian mutation Acceleration Coefficient Best Position 2 weights Cooperation Coefficient 2 Randomly generated between the upper and Initial swarm population lower bounds of the variables Stopping Criteria 150 iterations Max. velocity Refer to Section III Min. velocity Refer to Section III  20 particles  150 iterations  No benefit for more iterations /particles 13 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Case study – Results  Energy resources schedule PSO schedules all the resources but not all the available capacity 14 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Case study – Results  Feeder 1 MC consumers schedule in RedA program Some of the consumers are not scheduled by PSO 15 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Case study – Results R500 and R800  Resources schedule costs Differences between methods related to the resources schedule 16 THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES