Robustness and Cost Efficiency through User Flexibility in the Distribution Network

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Washington, April 20, 2015 Robustness and Cost Efficiency through User Flexibility in the Distribution Network Knut Samdal, Research Director SINTEF Energy Research knut.samdal@sintef.no 1

SINTEF is the largest independent research organisation in Scandinavia Leading expertise in the natural sciences and technology, environment, health and social science 2100 employees from 70 countries Annual sales of NOK 3 billion (EUR 400 mill) customers in more than 60 countries A non-commercial research foundation with subsidiaries

Our partnership with NTNU functions at both strategic and operational levels Strategic coordination NTNU personnel work on SINTEF projects SINTEF staff teach at NTNU Joint use of laboratories and equipment Cooperation between SINTEF and NTNU has developed over 60 years

We are among Europe s largest contract research organisations

In times with uncertainty increasing the need for flexibility is apparent. Today (2008) Tomorrow (2025) 4500 4000 20% wind power 20% wind power 4500 4000 50% wind power 3500 3500 3000 3000 2500 2500 2000 2000 1500 1500 1000 1000 500 500 0 0 Wind power Demand Wind power Demand Wind power covers the entire demand for electricity in 200 hours (West DK) In the future wind power will exceed demand in more than 1,000 hours The Wind Power Challenge - An illustrative case from Denmark 5

The philosophical view Philosophy: "the most basic beliefs, concepts, and attitudes of an individual or group" Mirriam-Webster on-line dictionary. Two archetypes. 1. Smart grids is about designing a power system bottom-up, where all control actions are taken de-centralized by local intelligence in a coordinated manner. 2. The widespread penetration of Smart grids functionalities (DG, electric vehicles, demand response, etc) calls for designing top-down centralized control actions ensuring reliability and security of supply. 6

SINTEFs' view on Smart grids It's not a matter of choice, but of coordination. 1. To be able to control, we need to be able to observe. 2. To be able to act upon observations we need to have control actions at hand. 3. To be able to have control actions at hand, inherent power system flexibility must be designed for. In a smart grid, observations and control actions can be put in place at every level, and coordination must ensure that the most adequate resources are brought into play when needed regardless of it's "distributed" or "centralized" nature. 7

Electricity consumption in Norway Total 127 TWh (07) Heating: appr. 35 TWh Large industrials: appr. 40 TWh Peak load: appr 24 000 MW (h10) A large (theoretical) DR potential Industry ~3 000 MW Residential and commercial: 1 700 MW Production (99 % Hydro): average 130 TWh/Year 50 TWh variation between wettest and driest year 8

Household electricity consumption in Norway Average annual electricity consumption in domestic sector: 16 000 kwh per household Source: Statistics Norway Approx 80% of electricity consumption relates to water and space heating. Source: : EU/ REMODECE (2006/2007) 9

Empirical load shifting potential from water heaters in Norway Peak power response, remote load control, different houes 700 18% 30% 15% 2% Coincidence factor 600 Watt 500 400 300 200 100 0 System peak hour 7-8 8-9 9-10 10-11 600 MWh/h load reduction in peak load provided contribution from 50 % of the Norwegian Households 10

Demand response - Situational change in consumption Research projects at SINTEF Energy Research End-user market (1996-2000) The customer was discovered Mapping of theoretical potential for demand response Demand response by efficient use of ICT (2001-2004) Large scale testing of technology, load control and price incentives Market Based Demand Response (2005-2008) Several smaller pilots related to the power situation and price incentives Environmental benefits by smart meters (2009-2012) Increase customer awareness of electricity consumption (display) Smart meters as a part of Smart grids DeVID (2012-2015) Demand Response (load control, storage, PV) in the distribution grid Use case-methodology New data from smart meters and smart substations 11

EcoGrid EU A Prototype for Smart Grids FP7- Energy 2010 Project period: Mid 2011 Late 2015 Total budget: 21 million Euro (EU: 12,7 million Euro) Project initiated by Energinet.dk (Danish TSO) Project coordinated by SINTEF Energy Research 15 partners http://www.eu-ecogrid.net/

EcoGrid EU in Brief A large scale demonstration of a real-time marketplace for distributed energy resources (DER) The demo site is the island of Bornholm - a real power system with more than 50 % renewable energy connected to the Nordic synchronous system ICT systems and innovative market solutions enable small consumers to offer TSOs additional and more efficient balancing services The EcoGrid EU concept meets the increasing need for balancing services 13

14 14

Active use of passive resources 15

The Fundamental Idea of EcoGrid System balance Price signal 16

The Scope of a Real-time Market 17

Why a Real-time Market? An efficient way to meet the future challenge of balancing High(er) demand for flexible consumption/production High(er) volatility High(er) balancing cost An efficient instrument to wide spread adoption of small-scale end-users/ prosumers in the power market(s) Increasing competition in the power market(s) Small scale end-users can attain economic benefits TSOs get access to alternative balancing resources Design of an EcoGrid prototype real-time market place is a realistic approach because it is "just" widening the scope of the existing power market 18

2000 Participating Customers in the Demonstration Statistic Control 200 households with smart meters No access to specific information Manual Control 500 households with smart meters Receiving simple market price information Must move their energy consumption on their own Automatic Control 700 automated households with IBM-Green Wave Reality equipment and smart meters All houses have heat pumps or electric heating responding autonomously to price signals Aggregated automatic Control 500 automated households with Siemens equipment and smart meters All houses have heat pumps or electric heating responding to control signals Smart Businesses Up to 100 costumers with smart meters Including small business and public customers Connected smart appliances responding to control signals 20

Load response time series

Conclusions The electric energy sector faces substantial challenges in the coming years related to the implementation of Smart meters and other Smart grids technologies Peak load and intermittent generation are major challenges to the grid Power driven, not energy driven Accumulated potential for demand response is large also from smaller customers Flexibility is a key asset with high responsiveness Demand response can be realized if the customers get sufficient incentives Demand response can be integrated into the power market a relative small decline in demand can contribute to substantial reductions in price in shortage situations can also be used to solve grid problems (bottlenecks in peak load periods)

Future work Past projects: concepts, potential, feasibility Current projects: infrastructure, framework, regulatory mechanisms Future work: Optimization Automation Formalization 23

Thank you for your attention! Knut.Samdal@sintef.no 24