05 Simonis Thermal Energy Flow Balancing CESBP.ppt...

Thermal Energy Flow Balancing for Optimizing

Energy Performance in Public Swimming Pools with

Solar Thermal Micro Generation

C. de Torre, A. Macia, M.A. Garcia-Fuentes, C. C. de Torre, A. Macia, M.A. Garcia-Fuentes, C.

Valmaseda

CARTIF Foundation, Energy Division, Valladolid,

Spain

H.Simonis

University College Cork, Ireland

Purpose of study

• Look at one site in more detail

– Test software and algorithms

• Understand factors influencing project progress

– Process requirements for potential roll-out

– Investment cost/effort of project implementation– Investment cost/effort of project implementation

Demo Site

• Huerta del Rey Sports Arena, Valladolid, Spain

– Indoor swimming pool

– Sports arena

– Gymnasium

– Outdoor courts

– Office space

Use Case

• Heating of swimming pool

• Either by

– Gas fired boilers

– Solar thermal array on roof

• Current control• Current control

– Maintain pool temperature at all times

• Idea:

– Use occupancy and weather data

• Best use of solar thermal array

– Storage of solar thermal energy

• Storage tanks and pools themselves

High-level Schematic

Cost of running the sports centre: Gas

20,000.00

30,000.00

40,000.00

50,000.00

60,000.00Consumos Gas en M3 C. D. HUERTA DEL REY

Enero Febrero Marzo Abril Mayo Junio Julio Agosto Septiembre Octubre Noviembre Diciembre

Año 2005 53,758.00 40,266.00 39,286.00 33,719.00 17,303.00 8,816.00 7,434.00 6,910.00 11,282.00 15,279.00 37,754.00 40,804.00

Año 2006 53,363.00 45,229.00 37,948.00 29,077.00 17,112.00 7,814.00 2,743.00 4,148.00 4,802.00 14,067.00 20,964.00 27,946.00

Año 2007 50,991.00 36,585.00 34,926.00 26,163.00 17,764.00 12,061.00 5,059.00 3,495.00 5,839.00 13,902.00 34,874.00 27,119.00

Año 2008 34,219.51 20,369.00 0.00 0.00 4,319.00 12,539.25 307.75 3,087.00 5,618.00 17,732.22 23,519.78 28,832.00

Año 2009 43,544.00 34,353.00 23,777.00 19,736.00 9,106.00 890.00 116.00 0.00 37.00 1,641.00 14,543.00 22,106.00

Año 2010 27,696.00 22,870.00 17,432.00 14,283.00 9,047.00 3,853.00 581.00 47.00 220.00 7,795.00 20,296.00 23,233.00

Año 2011 21,263.00 21,163.00 20,501.00 10,347.00 6,526.00 5,258.00 2,523.00 1,680.00 3,613.00 4,573.00 12,007.00 23,824.00

Año 2012 29,629.00 22,637.00 24,505.00 13,445.00 9,286.00 5,045.00 4,516.00 3,477.00 2,275.00 11,579.00 24,081.00 19,083.00

Año 2013 31,803.00 23,360.00 18,137.00

0.00

10,000.00

20,000.00

Implementation

• Data collection

– Existing infrastructure for temperature measurements

– Integrated in existing BMS

– Sensors only maintained if essential for control

• New sensors for energy flows• New sensors for energy flows

– Flow meters/Energy meters

• Requirements for high-quality data collection

– Synchronized measurements

– How much redundancy can be provided?

Web-based Data Visualization

• Access to sensor data

– Temperature

– Pump status

– Valve status

– Flow rates

– Energy flows

Data Access from Solver (Solar Irradiance)

Affecting the BMS

• Optimization module suggests operating modes

• Does not replace existing control

• Change set-points or rules of controller

• Access control issues for BMS

– Who can authorize changes?– Who can authorize changes?

– How to avoid run-away behaviour

Energy Flow Model

• Energy Sources

– Solar thermal array

– Boilers

• Storage Components

– Tanks– Tanks

– Pool

• Sinks

– DHW Use

– Pool Hall Environment

• Flows/Heat Transfer

Component-Based Model

• Model is described as combination of

– Components

– Flows connecting components

• Component library

– Extension by inheritance and composition– Extension by inheritance and composition

• Mathematical model is generated automatically

– Code for mixed integer programming (CPLEX)

– Documentation of model

Resulting MIP Model

• Time

– Discrete time periods (15 min)

• Variables

– Flows between components

– Vectors of values– Vectors of values

• Constraints

– Energy conservation

– Heat transfer

• Objective

– Sum of objectives of components

Levels of Abstraction

• Solver module abstracts most details

– Not automated

• Correspondence to detailed control/sensor model

required

– Partially automated

• Translate solver decisions translate into control changes

– Human understanding of design

Forecast Data Required

• Solar irradiance

– Really: Forecast of thermal array output

• Temperature

– Heat-loss of swimming pool

• Occupancy• Occupancy

• Domestic hot water (DHW)

• (Electricity Price Prediction)

• (Electricity Demand Prediction)

Solar Irradiance Forecast for Valladolid

Temperature Forecast Valladolid

Component Reuse

• Key to economic success

• All demonstrators share optimization tools

– Models generated from declarative description

• Reuse of forecasting modules

• Possible through middleware integration• Possible through middleware integration

Occupancy Sports Arena (Handball)

Occupancy Sports Arena

• Use of arena known well in advance

• Two main types of activities

– Games (teams, spectators)

– Training (teams)

• DHW use follows team schedule• DHW use follows team schedule

Occupancy Swimming Pool

Occupancy Swimming Pool (II)

• No sensor data for direct measurement of occupancy

– Turnstile

– Card reader

• Clear calendar for group activities (courses/clubs)

– Majority of users– Majority of users

• Individual users difficult to predict

– No ground truth

– Small numbers make forecasting hard

• Individual decisions have large effect

– Impact on total user numbers not too significant

Contracting Schedules

• The optimization is based on imprecise forecast data

• How accurate is the schedule, compared to hypothetical

solution with perfect knowledge?

• Evaluation Strategy:

Surprising Results

• UCC CHP plant (Impact of price and demand forecast)

Similar Results

• Manufacturing Scheduling

– Ifrim, O’Sullivan & Simonis, 2012

• Home Energy Management System, EV Charging

– Grimes, Simonis, Pratt & Sheridan, 2012

• Good schedules can be found, even if forecasts are

imprecise

• High quality even when compared to hypothetical

schedule based on ex-post data

Conclusions

• Work in progress

– Installation of sensors

– Connection of middleware

– Access through middleware

– Evaluation of predictors

– Integration with solver

• Lead-time requirement for forecasting

– Training data, historical data needs

• Change process issues

– Investment sign-off

– Changes to existing systems

The research presented is supported by a fund

from Seventh Framework Program – ICT

“Control & Automation Management of

Buildings & Public Spaces in the 21st Century”

or CAMPUS 21

(Project-Nr: 285729).(Project-Nr: 285729).