.. SPDX-FileCopyrightText: ASSUME Developers .. .. SPDX-License-Identifier: AGPL-3.0-or-later Bidding Strategies ================== Bidding strategies are a core concept of ASSUME which describe how agents bid their assets on a market. Overview --------- As described in the :ref:`exchangeable_bidding_strategy`, a Bidding Strategy dictates the bidding behavior of units in different markets, whereby it maps certain states and technical constraints to bidding decisions. In general, there is a distinction between two kinds of strategy classes: - :py:meth:`assume.strategies.portfolio_strategies.UnitOperatorStrategy`, which indicate the use in a UnitsOperator and can be used to provide a Portfolio optimization. - Strategies used for a single unit of :doc:`units`, which range from naive (e.g. :py:meth:'assume.strategies.naive_strategies.EnergyNaiveStrategy')to advanced (e.g. :py:meth:'assume.strategies.advanced_orders.EnergyHeuristicFlexableLinkedStrategy'). Both types have a function `calculate_bids` which is called with the information of the market and bids to bid on. UnitOperatorStrategy -------------------- The UnitsOperatorStrategies can be used to adjust the behavior of the UnitsOperator. The default is the :py:meth:`assume.strategies.portfolio_strategies.UnitsOperatorDirectStrategy`. It formulates the bids to the market according to the bidding strategy of the each unit individually. This calls `calculate_bids`` of each unit and returns the aggregated list of all individual bids of all units. This is the default for all UnitsOperators which do not have a separate strategy configured. Another implementation includes the :py:meth:`assume.strategies.portfolio_strategies.UnitsOperatorEnergyHeuristicCournotStrategy` that adds a markup to the marginal cost of each unit of the units operator. The marginal cost is computed with EnergyNaiveStrategy and the markup depends on the total capacity of the unit operator. UnitStrategies -------------- The ASSUME framework provides multiple options in terms of Bidding Strategy methodologies: ============================== ============================================================= Bidding Strategy Methodology Description ============================== ============================================================= Naive Simple bidding strategies for participating in a market mechanism that follows the Merit Order principle. No additional dependencies needed. Heuristic Basic methodology to form bids, based on participating in a market mechanism that follows the Merit Order principle. These strategies do not utilise forecasting or consider finer details such as the effects of changing a power plant's operational state (start-up costs etc.), so the bid volume is of the order of its maximum power capacity (given ramping constraints), and the bid price is set to the marginal cost. Optimization Methodology, based on the flexABLE methodology [`Qussous et al. 2022 `_], offers more refined strategising compared to the Naive methods. Applicable to power plant and storage units, it incorporates market dynamics to a greater degree by forecasting market prices, as well as accounting for operational history, potential power loss due to heat production, and the fact that a plant can make bids for multiple markets at the same time. Learning A :doc:`reinforcement learning ` (RL) approach to formulating bids for an Energy-Only Market. Agents perform actions (choose bid price(s) and for storage a direction) informed by observations (including forecasted residual load, forecasted price, marginal cost). Bid volumes are fixed to the maximum possible volume. Based on the reward (profits) from accepted bids, agents learn to optimise bids to maximise profits. This requires to have PyTorch installed. Interactive Strategies which let a user handle input through a terminal or other interface. ============================== ============================================================= For each Bidding Strategy methodology there are multiple Bidding Strategy options depending on the product and market that the bid is intended for, as well as the type of unit making the bid. Accordingly, each Bidding Strategy has an associated ID which takes the form "unit_product_method_comment", the comment being optional. This "bidding_strategy_id" needs to be entered when defining a unit's bidding strategy. Each Bidding Strategy and associated ID for each methodology is defined and described further below. When constructing a units CSV file, the bidding strategies are set using :code:`"bidding_*"` columns, where the market type the bidding strategy is applied to follows the underscore (the market names need to match with those in the config file). ====================== =========== ================================== ============================================ ============================================ =========== name technology bidding_EOM bidding_CRM_pos bidding_CRM_neg max_power ====================== =========== ================================== ============================================ ============================================ =========== Naive-Bidding Unit hydro powerplant_energy_naive powerplant_capacity_heuristic_balancing_pos powerplant_capacity_heuristic_balancing_neg 1000 Advanced-Bidding Unit hydro powerplant_energy_heuristic_block powerplant_capacity_heuristic_balancing_pos powerplant_capacity_heuristic_balancing_neg 1000 ====================== =========== ================================== ============================================ ============================================ =========== We'll now take a look at the different Bidding Strategies within each methodology, their associated "bidding_strategy_id", and for which unit and market type(s) they are valid. Naive ----- ================================== ======================= ================================================================================================================================== bidding_strategy_id For Market Types Description ================================== ======================= ================================================================================================================================== powerplant_energy_naive EOM, CRM_pos, CRM_neg Basic strategy for merit order markets at a single timepoint (hour). Uses marginal cost as bid price and the maximum feasible power (respecting ramping constraints) as bid volume. demand_energy_naive EOM, CRM_pos, CRM_neg Basic naive strategy for demand units. Can realise a price-inelastic demand by setting a very high bid price and volume equal to demand at the timepoint. powerplant_energy_naive_otc OTC Similar to powerplant_energy_naive but for OTC (bilateral) trades. demand_energy_naive_otc OTC Similar to demand_energy_naive but for OTC (bilateral) trades. powerplant_energy_naive_profile EOM, CRM_pos, CRM_neg Similar to powerplant_energy_naive but submitted as a 24-hour block (Day-Ahead). Bid price is set to the marginal cost at the starting timepoint. powerplant_energy_naive_redispatch redispatch Submits unit info and currently dispatched power for upcoming hours to the redispatch market (includes marginal cost, ramping, and dispatch information). demand_energy_naive_redispatch redispatch Submits unit info and currently dispatched power for upcoming hours to the redispatch market (includes marginal cost, ramping, and dispatch information). household_energy_naive_redispatch redispatch Naive DSM strategy for industry/household units; bids available flexibility on redispatch. Volume equals flexible power at product start; price equals marginal cost at product start. industry_energy_naive_redispatch redispatch Naive DSM strategy for industry/household units; bids available flexibility on redispatch. Volume equals flexible power at product start; price equals marginal cost at product start. exchange_energy_naive EOM Incorporates cross-border trading: submits export (negative volume) and import bids. Exports are treated as demand with very high bid price; imports as supply with a very low bid price to virtually guarantee acceptance. ================================== ======================= ================================================================================================================================== Naive method API references: - :py:meth:`assume.strategies.naive_strategies.EnergyNaiveStrategy` - :py:meth:`assume.strategies.extended.EnergyNaiveOtcStrategy` - :py:meth:`assume.strategies.naive_strategies.EnergyNaiveProfileStrategy` - :py:meth:`assume.strategies.naive_strategies.EnergyNaiveRedispatchStrategy` - :py:meth:`assume.strategies.naive_strategies.DsmEnergyNaiveRedispatchStrategy` - :py:meth:`assume.strategies.naive_strategies.ExchangeEnergyNaiveStrategy` Heuristic --------- ============================================ ================== ================================================================================================================== bidding_strategy_id For Market Types Description ============================================ ================== ================================================================================================================== demand_energy_heuristic_elastic EOM This bidding strategy is formulated for a demand unit to realise a "price-elastic" demand bid, approximating a marginal utility curve. The elasticity can be set to "linear" or "isoelastic". powerplant_energy_heuristic_flexable EOM A more refined approach to bidding on the EOM compared to naive. A unit submits both inflexible and flexible bids per hour. The inflexible bid represents the minimum power output, priced at marginal cost plus startup costs, while the flexible bid covers additional power up to the maximum capacity at marginal cost. It incorporates price forecasting and accounts for ramping constraints, operational history, and power loss due to heat production. powerplant_energy_heuristic_block EOM A power plant strategy valid for complex market clearing which bids dict blocks to the market. The bid is for a block of multiple hours instead of being for a single hour. A minimum acceptance ratio (MAR) defines how to handle rejected bids within individual hours of the block. For the inflexible bid, the MAR is set to 1, meaning all bids within the block must be accepted otherwise the whole block bid is rejected. A separate MAR can be set for children (flexible) bids. See the Advanced Orders tutorial: https://assume.readthedocs.io/en/latest/examples/06_advanced_orders_example.html#1.-Basics powerplant_energy_heuristic_linked EOM A power plant strategy which handles block and linked bids on a market with these fields as a dict. The strategy is similar to :code:`powerplant_energy_heuristic_block` but allows to integrate the flexible bids as linked bids. See the Advanced Orders tutorial: https://assume.readthedocs.io/en/latest/examples/06_advanced_orders_example.html#1.-Basics powerplant_capacity_heuristic_balancing_neg CRM_neg A bid on the negative Capacity or Energy Control Reserve Market (CRM); volume is determined by calculating how much it can reduce power. The capacity price is found by comparing the revenue it could receive if it bid this volume on the EOM; the energy price is the negative of marginal cost. powerplant_capacity_heuristic_balancing_pos CRM_pos A bid on the positive Capacity or Energy CRM; volume is determined by calculating how much it can increase power. The capacity price is found by comparing the revenue it could receive if it bid this volume on the EOM; the energy price is the positive of marginal cost. storage_energy_heuristic_flexable EOM Determines strategy of a Storage unit bidding on the EOM. The unit acts as a generator or load based on the average price forecast. If the current price forecast is greater than the average price, the Storage unit will bid to discharge at a price equal to the average price divided by the discharge efficiency. Otherwise, it will bid to charge at the average price multiplied by the charge efficiency. Calculates ramping constraints for charging and discharging based on theoretical state of charge (SOC), ensuring that power output is feasible. The bid volume is subject to the charge/discharge capacity of the unit. storage_capacity_heuristic_balancing_neg CRM_neg Analogous to :code:`storage_energy_heuristic_flexable`, but bids either on the negative capacity CRM or energy CRM. storage_capacity_heuristic_balancing_pos CRM_pos Analogous to :code:`storage_energy_heuristic_flexable`, but bids either on the positive capacity CRM or energy CRM. ============================================ ================== ================================================================================================================== Heuristic method API references: - :py:meth:`assume.strategies.naive_strategies.EnergyHeuristicElasticStrategy` - :py:meth:`assume.strategies.advanced_orders.EnergyHeuristicFlexableBlockStrategy` - :py:meth:`assume.strategies.advanced_orders.EnergyHeuristicFlexableLinkedStrategy` - :py:meth:`assume.strategies.flexable.CapacityHeuristicBalancingNegStrategy` - :py:meth:`assume.strategies.flexable.CapacityHeuristicBalancingPosStrategy` - :py:meth:`assume.strategies.flexable_storage.StorageEnergyHeuristicFlexableStrategy` - :py:meth:`assume.strategies.flexable_storage.StorageCapacityHeuristicBalancingNegStrategy` - :py:meth:`assume.strategies.flexable_storage.StorageCapacityHeuristicBalancingPosStrategy` Optimization ------------ =========================================== ================== ============ bidding_strategy_id For Market Types Description =========================================== ================== ============ household_energy_optimization EOM An energy strategy of a Household DSM unit. The bid volume is the optimal power requirement of the optimization. industry_energy_optimization EOM An energy strategy of a Industry DSM unit. The bid volume is the optimal power requirement of the optimization. household_capacity_heuristic_balancing_neg CRM_neg A negative capacity strategy of a Household DSM unit. The bid volume is the optimal power requirement of the optimization. household_capacity_heuristic_balancing_pos CRM_pos A positive capacity strategy of a Industry DSM unit. The bid volume is the optimal power requirement of the optimization. industry_capacity_heuristic_balancing_neg CRM_neg A negative capacity strategy of a Household DSM unit. The bid volume is the optimal power requirement of the optimization. industry_capacity_heuristic_balancing_pos CRM_pos A positive capacity strategy of a Industry DSM unit. The bid volume is the optimal power requirement of the optimization. powerplant_energy_optimization_dmas EOM Power plant strategy using forecast optimization and avoided cost calculation used for smart bids coming from the DMAS methodology storage_energy_optimization_dmas EOM Storage strategy using forecasts and avoided cost calculation used for smart bids coming from the DMAS methodology =========================================== ================== ============ Optimization method API references: - :py:meth:`assume.strategies.naive_strategies.DsmEnergyOptimizationStrategy` - :py:meth:`assume.strategies.naive_strategies.DsmCapacityHeuristicBalancingPosStrategy` - :py:meth:`assume.strategies.naive_strategies.DsmCapacityHeuristicBalancingNegStrategy` - :py:meth:`assume.strategies.dmas_powerplant.EnergyOptimizationDmasStrategy` - :py:meth:`assume.strategies.dmas_storage.StorageEnergyOptimizationDmasStrategy` Learning -------- ===================================== ================== ============================================================= bidding_strategy_id For Market Types Description ===================================== ================== ============================================================= powerplant_energy_learning EOM A :ref:`reinforcement learning ` (RL) approach to formulating bids for a Power Plant in an Energy-Only Market. The agent's actions are two bid prices: one for the inflexible component (P_min) and another for the flexible component (P_max - P_min) of a unit's capacity. The bids are informed by 50 observations, which include forecasted residual load, forecasted price, total capacity, and marginal cost, all contributing to decision-making. Noise is added to the action, especially towards the beginning of the learning, to encourage exploration and novelty. The reward is calculated based on profits from executed bids, operational costs, opportunity costs (penalizing underutilized capacity), and a regret term to minimize missed revenue opportunities. This approach encourages full utilization of the unit's capacity. storage_energy_learning EOM Similar RL approach as :code:`learning_eom_powerplant`, for a Storage unit. The make-up of the observations is similar to those for :code:`learning_eom_powerplant`, with an additional observation being the State-of-Charge (SOC) of the storage unit. The agent has 2 actions - a bid price, and a bid direction (to buy, sell or do nothing). The bid volume is subject to the charge/discharge capacity of the unit. The reward is calculated based on profits from executed bids, with fixed costs for charging/discharging incorporated. powerplant_energy_learning_single_bid EOM Reinforcement Learning Strategy with Single-Bid Structure for Energy-Only Markets. This strategy is a simplified variant of the standard `EnergyLearningStrategy`, which typically submits two separate price bids for inflexible (P_min) and flexible (P_max - P_min) components. Instead, `EnergyLearningSingleBidStrategy` submits a single bid that always offers the unit's maximum power, effectively treating the full capacity as inflexible from a bidding perspective. renewable_energy_learning_single_bid EOM Reinforcement Learning Strategy for a renewable unit that enables the agent to learn optimal bidding strategies on an Energy-Only Market. ===================================== ================== ============================================================= Learning method API references: - :py:meth:`assume.strategies.learning_strategies.EnergyLearningStrategy` - :py:meth:`assume.strategies.learning_strategies.EnergyLearningSingleBidStrategy` - :py:meth:`assume.strategies.learning_strategies.StorageEnergyLearningStrategy` - :py:meth:`assume.strategies.learning_strategies.RenewableEnergyLearningSingleBidStrategy` Other ----- =============================== ================ ================== ============================================================= bidding_strategy_id For Unit Types For Market Types Description =============================== ================ ================== ============================================================= powerplant_energy_interactive Any Any The bidding volume and price is manually entered in the terminal. =============================== ================ ================== ============================================================= Miscellaneous method API references: - :py:meth:`assume.strategies.interactive_strategies.EnergyInteractiveStrategy`