AI for a Responsible Power System Mathijs de Weerdt Associate Professor in Algorithmics group, EEMCS Delft University of Technology February 21, 2019
My talk in a nutshell • Artificial Intelligence can (should) contribute towards a more responsible society. • Algorithmic innovations can tackle concrete AI challenges in the power system. 2 / 27
Current Allocation is Not Responsible • Part of humanity is starving • Part of humanity consumes too much • Running out of some of the resources Mainly caused by optimization of profit. Doughnut Economics by Kate Raworth [2017] 3 / 27 www.kateraworth.com/
Current Allocation is Not Responsible • Part of humanity is starving • Part of humanity consumes too much • Running out of some of the resources Mainly caused by optimization of profit. We need more responsible allocations: 1 more fair across parties 2 better balance of optimal now versus long-term effects Does this rise new algorithmic challenges? Doughnut Economics by Kate Raworth [2017] 3 / 27 www.kateraworth.com/
Scientific gap allocation decision support well understood simple complex AI & algorithmics 4 / 27
Scientific gap allocation decision support with interaction well understood (behavioural) game theory simple complex AI & algorithmics algorithmic game theory Algorithmic game theory: • Fair allocation under strict conditions [Bergemann and V¨ alim¨ aki, 2010, Parkes et al., 2010] • For relevant settings: impossibility theorems [Satterthwaite, 1975]. 4 / 27
Scientific gap allocation decision support with interaction well understood (behavioural) game theory simple complex AI & algorithmics algorithmic game theory Algorithmic game theory: • Fair allocation under strict conditions [Bergemann and V¨ alim¨ aki, 2010, Parkes et al., 2010] • For relevant settings: impossibility theorems [Satterthwaite, 1975]. But these are situations we encounter — and deal with — in practice. • Can algorithms and AI help to improve this current practice with respect to efficiency, fairness, and longer term consequences. . . ? • Let’s look at some more concrete computational challenges in the electricity grid. 4 / 27
Today’s Electricity System
Today’s Electricity System • Electricity systems support the generation, transport and use of electrical energy. • They are large and complex and provide for everyone. Energy generated = energy consumed at all times How it used to be. . . • demand is predictable (at an aggregate level) • which generators are used is decided one day in advance ( unit commitment ) • minor corrections are made, based on frequency (primary control, secondary control, etc.) • a market with few actors (energy retailers) 6 / 27
The Energy Transition
The Energy Transition Changes in the power system • renewable energy is • intermittent • uncertain • uncontrollable • sometimes located in the distribution grid, and • has virtually no marginal costs • new loads such as heat pumps, airconditioning, and electric vehicles are • significantly larger than other household demand, and • more flexible (and therefore also less predictable) commons.wikimedia.org/wiki/File: These new loads can also be part of the solution! Electric_Car_recharging.jpg 9 / 27
Consequences for the main stakeholders Focus on (computational) challenges regarding 1 Wholesale market operators and system operators 2 Aggregators of flexible demand 3 Distribution network operators 10 / 27
Challenges in Wholesale Market Design Challenges for Market Operators/Regulators and ISO/TSO 1 more accurate models for bidding and market clearing • use finer granularity, power-based instead of energy-based (Philipsen et al., 2018) • deal with intertemporal dependencies caused by flexible shiftable loads • model stochastic information explicitly but reasonable models are non-linear: interesting optimization problem 2 allow smaller, local producers and flexible loads (scalability) 11 / 27
Aggregators of flexible demand New flexible loads can be used to match renewable generation, but • consumers do not want to interact with the market, and • markets do not want every consumer to interact. Challenges for Aggregators (a new role!): demand-side management 1 design mechanism to interact with consumers with flexible demand 2 interact with both wholesale markets and distribution service/network operator 3 optimize use of (heterogeneous) flexible demand under uncertain prices and uncertain consumer behavior 12 / 27
Challenges for DSOs Aim to avoid unnecessary network reinforcement by demand side management to resolve congestion and voltage quality issues Challenges for Distribution network system operators 1 (Close to) real-time coordination of generation, storage and flexible loads of self-interested agents to stay within network capacity limitations: • more agents than in traditional energy market • interaction with wholesale markets • communication may not be always reliable • more complex power flow computations (losses and limitations more relevant in distribution) 2 Long-term decision making under uncertainty 13 / 27
Challenges for DSOs Aim to avoid unnecessary network reinforcement by demand side management to resolve congestion and voltage quality issues Challenges for Distribution network system operators 1 (Close to) real-time coordination of generation, storage and flexible loads of self-interested agents to stay within network capacity limitations: • more agents than in traditional energy market • interaction with wholesale markets • communication may not be always reliable • more complex power flow computations (losses and limitations more relevant in distribution) 2 Long-term decision making under uncertainty Some of these challenges we take up in our research. 13 / 27
Research on Responsible Multi-Party Optimization Mission: to design (and understand fundamental properties of) planning and coordination algorithms for responsible optimization across organizational boundaries Scientific challenges in responsible multi-party optimization • efficiency (optimality) and scalability , 14 / 27
Research on Responsible Multi-Party Optimization Mission: to design (and understand fundamental properties of) planning and coordination algorithms for responsible optimization across organizational boundaries Scientific challenges in responsible multi-party optimization • efficiency (optimality) and scalability , • fairness , and • accounting for both long- and short-term effects. Example: Using Flexibility of Heat Pumps to Prevent Congestion (from the perspective of an aggregator working closely with network operator) 14 / 27
Heat Pumps to Prevent Congestion with Frits de Nijs, Erwin Walraven, and Matthijs Spaan [de Nijs et al., 2015, 2017, 2018a,b, 2019] De Teuge (near Zutphen) • pilot sustainable district in 2003 • heatpumps for heating But: at peak (cold) times, overload of electricity infrastructure 15 / 27
Potential Solutions 1 Reinforce network to cope with peak load 2 Optimal scheduling of demand 3 Re-allocation and online coordination 4 Pre-allocation and minimizing violations 16 / 27
2. Optimal Scheduling Formulate as a mixed integer problem (MIP) • decide when to turn on or off heat pump • minimise discomfort ( fair : squared distance to temperature set point) • subject to physical characteristics and capacity constraint MIP formulation h � cost ( θ t , θ set minimize ) (discomfort) t [ act 0 act 1 ··· act h ] t =1 θ t +1 = temperature ( θ t , act t , θ out subject to ) t n � act i , t ≤ capacity t i =1 act i , t ∈ [ off , on ] ∀ i , t 17 / 27
2. Optimal Scheduling Formulate as a mixed integer problem (MIP) • decide when to turn on or off heat pump • minimise discomfort ( fair : squared distance to temperature set point) • subject to physical characteristics and capacity constraint MIP formulation h � cost ( θ t , θ set minimize ) (discomfort) t [ act 0 act 1 ··· act h ] t =1 θ t +1 = temperature ( θ t , act t , θ out subject to ) t n � act i , t ≤ capacity t i =1 act i , t ∈ [ off , on ] ∀ i , t This scales poorly (binary decision variables: houses × time slots). But that is not the only problem. . . 17 / 27
3. Re-allocation and online coordination Not everything is known in advance (heat loss, available capacity), so adaptation may be required Arbitrage with Best Response (BR) 1 Plan each thermostat individually, as if unconstrained. 2 Look at the expected plan utility to determine action 3 Determine resource costs per time slot 4 Re-plan including these costs 18 / 27
3. Re-allocation and online coordination Not everything is known in advance (heat loss, available capacity), so adaptation may be required Arbitrage with Best Response (BR) 1 Plan each thermostat individually, as if unconstrained. 2 Look at the expected plan utility to determine action 3 Determine resource costs per time slot 4 Re-plan including these costs → Iterative process → Inspired by Brown’s Fictitious Play Keep all past realizations to ensure convergence 18 / 27
3. Re-allocation and online coordination Devices on (#) 150 Simulation of an 100 extreme scenario 50 • 182 households 0 0 6 12 18 24 30 36 42 48 • almost no capacity for heating 18–24 hours Time ( h ) 19 / 27
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