Entretiens du Centre Jacques Cartier Lausanne, 15-16 novembre 212 L électricité intelligente: vers des systèmes à valeur ajoutée Session 3: Gestion de la demande «Stockage par Supercondensateurs et Programmation Dynamique» A. Rufer
Une charge très typique avec de fortes variations de puissance (bidirectionnelles) : L ascenseur Un système à grande inertie: La cabine et son contre-poids sont accélérés et freinés 3 a) b) c)
Profils typiques des puissances
Optimal Energy Management of an Improved with Energy Storage Capacity based on Dynamic Programming E. Bilbao 1 P. Barrade 1 I. Etxeberria-Otadui 2 A. Rufer 1 S. Luri 2 I. Gil 3 1 Industrial Electronics Laboratory 2 IK4-IKERLAN 3 Innovation Centre STI-IEL-LEI, EPFL Technology Research Centre Orona EIC 115 Lausanne, Switzerland 25 Arrasate-Mondragón, Spain 212 Hernani, Spain IEEE Energy Conversion Congress & Exposition (ECCE) 212
Outline 1 Introduction 2 Objectives and Problem Statement 3 Dynamic Programming Based Energy Manager 4 Implementation and Validation 5 Conclusions 2 / 19
Outline 1 Introduction 2 Objectives and Problem Statement 3 Dynamic Programming Based Energy Manager 4 Implementation and Validation 5 Conclusions 3 / 19
Introduction Energy Efficiency sector (VDI 477 Part 1). Possible solution: To add an ESS. Regenerative energy recovering. Additional functionalities. Degrees of freedom increasing: Energy Management Strategy requirement. Rules-based, cutoff frequencies, fuzzy logic, ANN, DP. Grid Scaps Braking Resistor Objective The proposal, development, implementation and validation in a real test tower with energy storing capacity of an optimal Dynamic Programming based Energy Management Strategy. 4 / 19
Outline 1 Introduction 2 Objectives and Problem Statement 3 Dynamic Programming Based Energy Manager 4 Implementation and Validation 5 Conclusions 5 / 19
Objectives and Problem Statement Objectives for the Energy Storage System Operation To reduce energy losses in the braking resistor. To reduce short-term power peaks absorbed from the grid. Problem Statement SOC of the ESS is charged/discharged by: Grid energy: unidirectional and controllable by the EMS. energy: bidirectional and non-controllable (stochastic). Braking resistor: unidirectional and controllable by the EMS (security element). ESS State of Charge Operating Range SOC max SOC min 6 / 19
Statistical Energy Modeling Proposal of a General Energy and Statistical Description (GESD) GESD Representation of an The number of passengers and distance define the energy requirement (w k ): Traction mode: w k > Regenerative mode: w k < Probability of occurrence (P wk ): Several missions are repeated in a period of time, increasing the probability. Different missions have a similar energy requirement, increasing the probability. GESD can be updated on-line monitoring solely the elevator power profile. Probability (P wk ).36.24.12 GESD Representation -3 3 6 9 Energy Requirement (w k ) [kj] (Orona M34, 5 floors, 8 passengers) 7 / 19
Outline 1 Introduction 2 Objectives and Problem Statement 3 Dynamic Programming Based Energy Manager 4 Implementation and Validation 5 Conclusions 8 / 19
Dynamic Programming Based Energy Manager Dynamic Programming Principle Solving sequential decision problems. Economics and computer science applications. Breaking the sequence of problems in smaller ones. Subproblems are solved evaluating a cost function: Maximizing the benefits. Minimizing the costs. The solution is: Policy of decisions for Deterministic systems (DDP). Table of decisions for Stochastic systems (SDP). 9 / 19
Dynamic Programming Based Energy Manager Application with Energy Storing Capacity Sequential decision problem: energy absorbed from the grid. Stochastic Dynamic Programming (SDP): elevator energy requirements are unknown. The ( cost function based on the stock management theory: T otal ) ( Cost = V ariable ) ( Cost + Storage ) ( Cost + Shortage ) Cost Stochastic Cost Function Maps Circles are the states of the system: ESS state of charge. Transitions: elevator mission. Planes are the decisions: energy from the grid. 1 / 19
Outline 1 Introduction 2 Objectives and Problem Statement 3 Dynamic Programming Based Energy Manager 4 Implementation and Validation 5 Conclusions 11 / 19
Implementation DP based EMS Off-line implementation. Sliding window of 7 missions. EMS Cost Function Parameters: Variable cost c 1 Storage cost h 55 Shortage cost p 5 Scaps Energy (x k ) [kj] Table of Decisions DP Control Strategy 6 4 2 1 2 3 4 5 6 7 Mission (k) 4 3 2 1 Grid Energy Reference (u k ) [kj] Rules-based EMS Traction mode: the maximum grid power level is limited. P ESS = (P P Grid Limit ) P ESS < Regeneration mode: the energy is stored in the ESS. P ESS = P 12 / 19
Simulation Tests Grid Power Profile DP Power Profile 6 DP Power Profile 6 Peak reduction Grid Peak reduction 4 Grid 4 ESS ESS 2 2 ESS charging -2 ESS charging -2 [kw] -4-4 1 2 3 35 4 1 Time 2[s] 3 35 4 Time [s] [kw] 6 6 4 4 2 2-2 -2 Rules Power Profile Rules Power Profile Peak reduction Grid Peak reduction Grid ESS ESS -4-4 1 2 3 4 1 Time 2[s] 3 4 Time [s] Braking Resistor Energy Profile Simulation of a Mission Sequence 4 Random sequence of 8 missions. 3 (without ESS): 3kJ. Energy [kj] 2 DP based 1 EMS: 1.5kJ (-95%). Rules-based 2EMS: 4 6.4kJ 6 (-79%). 8 Energy [kj] 4 3 2 1 Rules DP 2 Braking Resistor Braking Resistor Energy Profile Rules DP Mission 4 Mission (k) 6 8 13 / 19
(simulation). (simulation). Introduction Statement Dynamic Programming The energy management Implementation algorithm has and been Validation also implemented and Conclusions validated exp real test tower: 18m, 8 passengers = 63kg (emulated by different loads) and Testbench and Experimental (Figure 6). Tests Three kinds of Description tests have been carried out with the same sequence of order to evaluate and quantify the real contribution of the proposed algorithm: without ESS (); (b) an elevator with ESS and a simple EMS based on r and (c) an elevator with ESS and an the proposed EMS based on DP (DP). Test Tower Main Characteristics model Orona M34 shaft length 18 [m] Number of floors 5 Cabin mass 8 [kg] Load mass (emulated by different loads) [:78.75:63] [kg] Number of passengers 8 Counterweight Medium ESS energy capacity 2 [Wh] ESS Controller Figure 6: Test tower with ESS. Experimental Tests of a Random Sequence of 8 Missions (a) An elevator without ESS (). (b) An elevator with ESS and the EMS based on rules (Rules). (c) An elevator with ESS and the EMS based on DP (DP). Cabin Loads 14 / 19
Introduction Statement Dynamic Programming Braking Implementation Resistor Power and Validation Profile Conclusions 3 2 Experimental Validation: Grid 1Power Smoothing 2 4 6 8 Power [kw] Power [kw] 6 64 42 2-2 Single Mission Peak reduction DP Power Profile Grid Peak reduction Grid ESS ESS -2-4 ESS charging 1 2 3 4-4 Time [s] 1 2 3 4 Time [s] -2 DP Power Profile ESS charging Rules Power Profile 6 Peak reduction Rules Power ProfileGrid 64 Peak reduction Grid ESS 42 ESS 2-2 -4 1 2 3 4-4 Time [s] 1 2 3 4 Time [s] Random 3 2 Sequence of Missions Rules 1 2 4 6 8 Summary of Experimental Results 3 2 Parameter 1 Rules DP Max. power 5.5 2 [kw] 4 3.7 6[kW] 81.9 [kw] Mission [kw] 1.8 [kw] 3.6 [kw] Smoothing level % 33 % 65 % 6 3 Grid Power Profile DP 2 4 6 8 6 3 Rules 2 4 6 8 6 3 DP 2 4 6 8 Mission (k) 15 / 19
Experimental Validation: Braking Resistor Energy Losses Braking Resistor Power Profile Reduction 1.5 Random Sequence of Missions 3 Summary of Experimental Results Parameter Rules DP Energy losses 228 [kj] 43 3 [kj] 37 [kj] [kj] 185 1.5 [kj] 191 [kj] Reduction level % 81 % 84 % 3 1.5 2 4 6 8 Rules 2 4 6 8 DP 2 4 6 8 Mission (k) 3 1.5 Braking Resistor Power Profile 2 4 6 8 3 1.5 Rules 2 4 6 8 3 1.5 DP 2 4 6 8 Mission (k) Energy [kj] 3 2 1 Braking Resistor Energy Profile Rules DP 2 4 6 8 Mission (k) 3 Braking Resistor Energy Profile 16 / 19
Outline 1 Introduction 2 Objectives and Problem Statement 3 Dynamic Programming Based Energy Manager 4 Implementation and Validation 5 Conclusions 17 / 19
Conclusions An optimal Dynamic Programming based Energy Management Strategy has been proposed, developed, implemented and validated in real test tower with energy storing capacity. The optimized control strategy for stochastic applications: Proposal of a General Energy and Statistical Description. The cost function is based on the stock management theory. A rules-based and Dynamic Programming based EMS have been simulated and validated experimentally in a sequence of 8 missions. The maximum grid power peak has been reduced by 33% (Rules) and by 65% (DP). The braking resistor energy losses have been reduced by 81% (Rules) and by 84% (DP). 18 / 19
Thank you for your attention 19 / 19