Entretiens du Centre Jacques Cartier

Entretiens du Centre Jacques Cartier

Lausanne,

15-16 novembre 2012

L’électricité intelligente: vers des

systèmes à valeur ajoutée

Session 3: Gestion de la demande

«Stockage par Supercondensateurs

et Programmation Dynamique»

3

a)

b)

c)

Une charge très typique avec de fortes variations

de puissance (bidirectionnelles) : L’ascenseur

Optimal Energy Management of an Improved

Elevator with Energy Storage Capacity based on

Dynamic Programming

E. Bilbao1 P. Barrade1 I. Etxeberria-Otadui2 A. Rufer1

S. Luri2 I. Gil3

1_{Industrial Electronics Laboratory} 2_IK4-IKERLAN 3_{Elevator Innovation Centre}

STI-IEL-LEI, EPFL Technology Research Centre Orona EIC 1015 Lausanne, Switzerland 20500 Arrasate-Mondrag´on, Spain 20120 Hernani, Spain

Innovación Tecnológica

Outline

1 Introduction

2 Objectives and Problem Statement

3 _{Dynamic Programming Based Energy Manager} 4 Implementation and Validation

Outline

1 Introduction

2 Objectives and Problem Statement

3 _{Dynamic Programming Based Energy Manager}

4 Implementation and Validation

Introduction

Energy Efficiency

Elevator sector (VDI 4707 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 Elevator 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.

Outline

1 Introduction

2 Objectives and Problem Statement

3 _{Dynamic Programming Based Energy Manager}

4 Implementation and Validation

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.

Elevator energy: bidirectional and non-controllable (stochastic). Braking resistor: unidirectional and

controllable by the EMS (security element).

Operating Range SOCmax SOCmin ESS State of Charge

Statistical Energy Modeling

Proposal of a

General Energy and Statistical Description (GESD)

GESD Representation of an Elevator

The number of passengers and distance

define the energy requirement (wk):

Traction mode: wk >0

Regenerative mode: wk<0

Probability of occurrence (Pwk):

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.

  -300 0 30 60 90 0.012 0.024 0.036 GESD Representation

Elevator Energy Requirement (w k) [kJ] El e va tor P rob ab ili ty ( Pwk )

Outline

1 Introduction

2 Objectives and Problem Statement

3 _{Dynamic Programming Based Energy Manager}

4 Implementation and Validation

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).

Dynamic Programming Based Energy Manager

Elevator 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.

Outline

1 Introduction

2 Objectives and Problem Statement

3 _{Dynamic Programming Based Energy Manager}

4 Implementation and Validation

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 Table of Decisions Scaps Energy (x k ) [kJ] Mission (k) DP Control Strategy 0 20 40 60 1 2 3 4 5 6 7

Grid Energy Reference (u

k ) [kJ] 0 10 20 30 40 Rules-based EMS

Traction mode: the maximum grid power level is limited. PESS =−(PElevator−PGrid Limit) ∀PESS <0

Regeneration mode: the energy is stored in the ESS. PESS =−PElevator

Simulation Tests

Grid Power Profile

-4 -2 0 2 4 6 Po w e r [ k W ] -4 -2 0 2 4 6 Po w e r [ k W ] 0 10 4 2 0 2 4 6 DP 0 10 4 2 0 2 4 6 Rul Peak Peak 20 Time [s] P Power Profi 20 Time [s] es Power Pro k reduction ESS ch k reduction 30 35 40 ile Grid Elevator ESS 30 40 ofile Grid Elevator ESS harging       -4 -2 0 2 4 6 Po w e r [ k W ] -4 -2 0 2 4 6 Po w e r [ k W ] 0 10 4 2 0 2 4 6 DP 0 10 4 2 0 2 4 6 Rul Peak Peak 20 Time [s] P Power Profi 20 Time [s] es Power Pro k reduction ESS ch k reduction 30 35 40 ile Grid Elevator ESS 30 40 ofile Grid Elevator ESS harging      

Braking Resistor Energy Profile

Simulation of a Mission Sequence

Random sequence of 80 missions. Elevator (without ESS): 30kJ. DP based EMS: 1.5kJ (-95%). Rules-based EMS: 6.4kJ (-79%).0 10 20 30 40 En e rg y [ k J ] 0 10 20 30 40 En e rg y [ kJ ] 0 20 0 0 0 0 0 Br Elevato Rules DP 0 20 0 0 0 0 0 Braking R Elevator Rules DP 40 Mission raking Resisto or 40 Mission (k) Resistor Energ r 60 80 or 60 80 gy Profile      

Introduction Statement Dynamic Programming Implementation and Validation Conclusions

Testbench and Experimental Tests Description

Test Tower Main Characteristics Elevator model Orona M34 Elevator shaft length 18 [m] Number of floors 5

Cabin mass 800 [kg]

Load mass (emulated

[0:78.75:630] [kg] by different loads)

Number of passengers 8 Counterweight Medium

ESS energy capacity 20 [Wh]

5. IMPLEMENTATION AND EXPERIMENTAL VALIDATION

Initially the proposed DP based EMS has been implemented and tested in simulation, using a basic elevator model and the GESD description presented in Figure 2. The cost function parameters implemented on the EMS are c = 1, h = 55 and p =5. Simulations show that it is possible to design the DP algorithm for a limited number of mission sequences instead of the whole set of missions, in order to improve computational cost without degrading the quality of the results. Therefore a seven mission sliding window has been implemented. Figure 4 shows the power peak reduction from the grid for a particular mission. Note that even if the elevator power is zero after the mission (between t =20s and t =35s), some power is being absorbed from the grid in order to charge the ESS. Figure 5 shows that braking losses can be reduced by 95% (from 30kJ to 1.5kJ); comparing an elevator without ESS and the improved elevator with a DP based EMS during the same random sequence of 80 missions.

Figure 4: Power profiles in a mission (simulation).

Figure 5: Braking resistor losses (simulation).

The energy management algorithm has been also implemented and validated experimentally in a real test tower: 18m, 8 passengers = 630kg (emulated by different loads) and 20Wh ESS [3], (Figure 6). Three kinds of tests have been carried out with the same sequence of 80 missions in order to evaluate and quantify the real contribution of the proposed algorithm: (a) an elevator without ESS (Elevator); (b) an elevator with ESS and a simple EMS based on rules (Rules) [3] and (c) an elevator with ESS and an the proposed EMS based on DP (DP).

Figure 6: Test tower with ESS.

0 10 20 30 35 40 -2 0 2 4 6 Time [s] P o w e r [k W ] DP Power Profile Grid Elevator ESS 0 20 40 60 80 0 10 20 30 40 Mission E n e rg y [ k J ] Braking Resistor Elevator DP Peak reduction ESS charging ESS Loads Elevator Controller Cabin

Experimental Tests of a Random Sequence of 80 Missions

(a) An elevator without ESS (Elevator).

(b) An elevator with ESS and the EMS based on rules (Rules).

Introduction Statement Dynamic Programming Implementation and Validation Conclusions

Experimental Validation: Grid Power Smoothing

Single Mission

    0 10 20 30 40 -4 -2 0 2 4 6 Time [s] Po w e r [ k W ] DP Power Profile Grid Elevator ESS 0 10 20 30 40 -4 -2 0 2 4 6 Po we r [ k W ]

Rules Power Profile Grid Elevator ESS ESS charging Peak reduction Peak reduction       0 10 20 30 40 -4 -2 0 2 4 6 Time [s] Po w e r [ k W ] DP Power Profile Grid Elevator ESS 0 10 20 30 40 -4 -2 0 2 4 6 Time [s] Po we r [ k W ]

Rules Power Profile Grid Elevator ESS ESS charging Peak reduction Peak reduction

Random Sequence of Missions

Summary of Experimental Results

Parameter Elevator Rules DP

Max. power 5.5 [kW] 3.7 [kW] 1.9 [kW] Smoothing level 0 [kW] 1.8 [kW] 3.6 [kW] 0 % 33 % 65 %             0 10 20 30 40 -4 -2 0 2 4 6 Time [s] Po w e r [ k W ] DP Power Profile Grid Elevator ESS 0 10 20 30 40 -4 -2 0 2 4 6 Time [s] Po we r [ k W ]

Rules Power Profile

Grid Elevator ESS 0 20 40 60 80 0 1 2 3

Braking Resistor Power Profile

Po w e r [k W ] Elevator 0 20 40 60 80 0 1 2 3 Po we r [ k W ] Rules 0 20 40 60 80 0 1 2 3 Po w e r [k W ] Mission DP 0 20 40 60 80 0 3 6

Grid Power Profile

Po w e r [ k W ] Elevator 0 20 40 60 80 0 3 6 Po w e r [ k W ] Rules 0 20 40 60 80 0 3 6 Po w e r [ k W ] Mission (k) DP ESS charging Peak reduction Peak reduction

Optimal Energy Management of an Improved Elevator with Energy Storage Capacity based on Dynamic Programming

Experimental Validation: Braking Resistor Energy Losses

Reduction

Random Sequence of Missions

Summary of Experimental Results

Parameter Elevator Rules DP

Energy losses 228 [kJ] 43 [kJ] 37 [kJ] Reduction level 0 [kJ] 185 [kJ] 191 [kJ] 0 % 81 % 84 %   0 20 40 60 80 0 1.5 3

Braking Resistor Power Profile

Po w e r [ k W ] Elevator 0 20 40 60 80 0 1.5 3 Po w e r [ k W ] Rules 0 20 40 60 80 0 1.5 3 Po w e r [ k W ] Mission (k) DP 100 200 300

Braking Resistor Energy Profile

En e rg y [ kJ ] Elevator Rules DP     0 20 40 60 80 0 1.5 3

Braking Resistor Power Profile

Po w e r [ k W ] Elevator 0 20 40 60 80 0 1.5 3 Po w e r [ k W ] Rules 0 20 40 60 80 0 1.5 3 Po w e r [ k W ] Mission (k) DP 0 20 40 60 80 0 100 200 300

Braking Resistor Energy Profile

En e rg y [ kJ ] Mission (k) Elevator Rules DP 16 / 19

Outline

1 Introduction

2 Objectives and Problem Statement

3 _{Dynamic Programming Based Energy Manager}

4 Implementation and Validation

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 80 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).