Claudio

Optimal Electrical Energy Management of Water Plant Facilities Claudio Ca単izares University of Waterloo Electrical & Computer Engineering ccanizar@uwaterloo.ca www.power.uwaterloo.ca www.wise.uwaterloo.ca

Outline • • • •

Motivations and objectives.

Demand side management (DSM).

Energy hub management systems (EHMS).

Water plant EHMS: – Optimization model – Example – Implementation

• Conclusions.

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Motivations and Objectives • Environmental, limited energy resources, and energy security concerns are driving programs for reduction of energy consumption: – – – – – – –

Global climate change.

Local and regional environmental issues (e.g. air quality).

Impact of fossil fuels (CO2, NOx, SOx).

Limited fossil fuel resources.

Increased energy costs.

Energy demand continuous growth.

Energy production and costs dependency on volatile international and national political issues.

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Motivations and Objectives • Reduce energy and environmental footprint can be accomplished through: – Efficient energy utilization.

– Distributed generation.

– Demand side management (DSM).

– Integration of various energy infrastructures (“energy hubs”).

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Demand Side Management (DSM) • DSM programs have been implemented since the 1960’s mainly as a result of: – Concerns about capacity scarcity.

– Critical need for managing the present and future electricity demand.

– Increased competition.

– Environmental concerns.

– Operational costs.

– Increased demand for reliable and cheap electricity.

– Deregulation.

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DSM • DSM in Ontario is driven by the need to reduce carbon footprint and new generation to supply peak load [IPSP, 2007]:

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DSM • DSM programs are designed to meet both the objectives of the utilities and the customers: – Utilities: Introduce elasticity in the load mainly to reduce costs, primarily through reduced losses and peak-power supply; prices and price volatility are reduced as well.

– Customers: Reduce energy bills, energy consumption, and/or carbon footprint.

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DSM • DSM programs are intended to reduce or shift the timing of energy consumption without significant interference with the normal activities of the customers.

• DSM programs consider the following aspects: – Technical.

– Economical.

– Customers’ “wants”.

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DSM •  From the perspective and interest of utilities, DSM programs are intended to achieve any/all of six load modification objectives:

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DSM • DSM programs’ classification:

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DSM • DSM programs have been targeted for the largest energy consumers: – For residential and commercial: • Refrigerators.

• Water heaters.

• Air conditioning systems.

– Agricultural: Mostly pumps.

– Industrial: Industry specific.

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DSM • Smart grids are facilitating DSM programs through: – “Smart”, controllable loads.

– Direct communication with loads through AMI.

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Energy Hub Management Systems (EHMS) • Consumers can be viewed as energy hubs demanding/supplying electricity, natural gas, heat, and possibly other energy vectors (e.g. hydrogen).

• Energy hub models can be developed for integrated and optimal energy management, considering the interactions among the various energy vectors.

• EHMS allow the optimal operation of energy hubs to reduce costs, energy consumption and/or GHG emissions.

• The optimal operation of energy hubs through EHMS is of particular relevance for DSM.

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EHMS • Main loads and processes need to be modeled:

– Main outputs, inputs and control variables need to be identified for optimization (modeling) purposes.

– Possible control variables are: • Voltage applied to the load.

• Production rates (e.g. water in a pumping facility).

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EHMS • Optimization model objective and constraints: – Objectives: • Minimize peak demand.

• Minimize energy consumption.

• Minimize switching operations of certain control devices.

– Some constraints: • • • • •

Production requirements.

Minimum material flow requirements.

Storage capacity.

Maximum production capacity.

Voltage limits.

• Measured data are required to: – Identify the mathematical model parameters.

– Validate models.

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Water Plant

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Water Plant • Control Variables: – Pump ON and OFF (switches).

– Water inflow to pump (gate valves).

• Objective and constraints: – Minimize 24-hour energy cost (based on the HOEP).

– Meet water demand requirements (forecasted 24hour in advance).

– Capacity of the reservoir (16,000 m3) and other flow constraints.

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Water Plant • Load Model: – Power consumption of each pump is represented as a function of water inflow rate and pump ON/OFF status.

– Model parameters are identified from detailed simulations of pumps, in lieu of measurements.

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Water Plant

25% max. energy cost savings 51% max. peak demand reduction

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Water Plant • Implementation issues: – Monitoring and control capability and equipment are required, but this is not a problem in most modern plants.

– Optimization models have to be implemented, tested, and validated.

– Required model parameters need to be determined from measurements.

– Historical detailed data such as water flows, pump operation, tank levels are required to establish a baseline.

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Water Plant –  Forecasts of some required input variables such as price have an impact:

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Conclusions • EHMS will be an integral part of DSM in the context of smart grids.

• Significant savings in both energy and demand charges can be expected for water plants.

• EHMS implementation and costs in modern water plants should be relatively straight forward and inexpensive.

• Of benefit to all: loads (municipalities), utilities, and the province.

• A win-win-win proposition!

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