Using of a cost-based unit commitment algorithm to assist bidding strategy decisions

In day-ahead markets, participants submit bids specifying the amounts of energy they wish to buy or sell and the price they are prepared to pay or receive. However, the dynamic for forming the Market Clearing Price (MCP) dictated by the bidding mechanism is frequently overlooked in the literature on energy market modeling. Forecasting models usually focus on predicting the MCP rather than trying to build the optimal supply and demand curves for a given price scenario. This article develops a data-driven approach for generating optimal offering curves using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm capable of handling continuous action spaces. Our model processes historical Italian electricity price data to generate stepwise offering curves that maximize profit over time. Numerical experiments demonstrate the effectiveness of our approach, with the agent achieving up to 85% of the normalized reward, i.e. the ratio between actual profit and the maximum possible revenue obtainable if all production capacity were sold at the highest feasible price. These results demonstrate that reinforcement learning can effectively capture complex temporal patterns in electricity price data without requiring explicit forecast models, providing market participants with adaptive bidding strategies that improve profit margins while accounting for production constraints. • Reinforcement Learning algorithm for optimizing bidding strategies of an electricity supplier. • Supplier submits offering curves based on historical electricity prices and production cost refining strategy through reward feedback. • Deep Deterministic Policy Gradient algorithm handles high-dimensional continuous action space, calibration with real-world data.

Problem statement: In a competitive electricity market with limited number of producers, Generation Companies (Gencos) is facing an oligopoly market rather than a perfect competition. Under oligopoly market environment, each Genco may increase its own profit through a favorable bidding strategy. The objective of a Genco is to maximize its profit and minimize the associated risk. In order to achieve this goal, it is necessary and important for the Genco to make optimal bidding strategies with risk management before bidding into spot market to get an expected high profit, since spot prices are substantially volatile. This study propose a method to build optimal bidding strategies in a day-ahead electricity market with incomplete information and considering both risk management and unit commitment. Approach: The proposed methodology employs the Monte Carlo simulation for modeling a risk management and a strategic behavior of rival. A probability density function (pdf), Value at Risk (VaR) and Monte Carlo simulation used to build optimal bidding strategies for a Genco. Results: The result of the proposed method shows that a Genco can build optimal bidding strategies to maximize expected total profit considering unit commitment and risk management. The Genco controls the risk by setting the confidence level. If the Genco increase the confidence level, the expected total VaR of profit decrease. Conclusions/Recommendations: The proposed method for building optimal bidding strategies in a day-ahead electricity market to maximize expected total profit considering unit commitment and risk management is helpful for a Genco to make a decision to submit bidding to the Independent System Operator (ISO).

In this paper, an optimal bidding strategy is proposed for microgrids that participate in both energy and spinning reserve markets for profit maximization. Various uncertain factors such as the day-ahead (DA) energy market prices, real-time (RT) energy prices, reserve prices, output power of the intermittent renewable resources and load demands are considered and modeled. The energy bids, reserve offers, unit commitment and power generation/consumption of the microgird are determined based on two-stage stochastic optimization. The first-stage is related to the DA market, while the second stage is related to the RT market. Comprehensive case studies, which are based on the codes and data of PJM market, have been carried out to verify the proposed strategy.

Virtual power plant (VPP) is an important way to solve the problem of distributed generation participating in power market transaction. However, the uncertainty of distributed renewable energy output brings high transaction risk to virtual power plant. For the VPP with renewable energy permeability, considering the uncertainty of renewable energy output, load and market price as well as the influence of demand-side resource participation in the power market, this paper puts forward the demand response bidding strategy of VPP under the open environment of electricity selling side. A multi-stage bidding strategy model is built for VPP participating in day-ahead energy market, intraday demand response market and real-time energy market, and solved based on stochastic programming theory. The results of the examples can support the effectiveness of the model and provide decision support for the participation of distributed energy and virtual power plants in power market transactions under the background of power system reform in China.

Approximations made in traditional day-ahead unit commitment model formulations can result in suboptimal or even infeasible schedules for slow-start units and inaccurate predictions of actual costs and wind curtailment. With increasing wind penetration, these errors will become economically more significant. Here, we consider inaccuracies from three approximations: the use of hourly intervals in which energy production from each generator is modeled as being constant; the disregarding of startup and shutdown energy trajectories; and optimization based on expected wind profiles. The results of unit commitment formulations with those assumptions are compared to models that: (1) use a piecewise-linear power profiles of generation, load and wind, instead of the traditional stepwise energy profiles; (2) consider startup/shutdown trajectories; and (3) include many possible wind trajectories in a stochastic framework. The day-ahead hourly schedules of slow-start generators are then evaluated against actual wind and load profiles using a model real-time dispatch and quick-start unit commitment with a 5min time step. We find that each simplification usually causes expected generation costs to increase by several percentage points, and results in significant understatement of expected wind curtailment and, in some cases, load interruptions. The inclusion of startup and shutdown trajectories often yielded the largest improvements in schedule performance.

A Lagrangian relaxation (LR) approach was presented which is with machine capacity relaxation and operation precedence relaxation for solving a flexible job shop (FJS) scheduling problem from the steelmaking-refining-continuous casting process. Unlike the full optimization of LR problems in traditional LR approaches, the machine capacity relaxation is optimized asymptotically, while the precedence relaxation is optimized approximately due to the NP-hard nature of its LR problem. Because the standard subgradient algorithm (SSA) cannot solve the Lagrangian dual (LD) problem within the partial optimization of LR problem, an effective deflected-conditional approximate subgradient level algorithm (DCASLA) was developed, named as Lagrangian relaxation level approach. The efficiency of the DCASLA is enhanced by a deflected-conditional epsilon-subgradient to weaken the possible zigzagging phenomena. Computational results and comparisons show that the proposed methods improve significantly the efficiency of the LR approach and the DCASLA adopting capacity relaxation strategy performs best among eight methods in terms of solution quality and running time.

A thermal unit commitment (UC) computer program has been used at the Riyadh Energy Control Centre of Saudi Consolidated Electric Company, Central Region, (SCECO Central) since 1982. Since then, system growth, plant aging, turbine degradation, ambient temperature effect on unit output, transmission constraints (line loading), MVAr/voltage control problem at light loads, and stringent operating requirements (spinning reserve, etc.) have imposed many constraints on the program, making it nearly impossible to produce a generation allocation schedule that is acceptable to system operators. A study undertaken to decide what should be done with the UC program and how the operator could be supplied with a daily generation schedule that is sufficiently accurate and practically feasible is described. Because it was not feasible to provide a new unit commitment program or to modify the existing one, it was decided to tailor the unit commitment input dataset such that the constraints are appropriately represented. In addition, it was decided to write a suite of computer programs (UCPROG) in FORTRAN that would process necessary input data and results of the UC program to achieve a more realistic, detailed, but precise unit allocation schedule.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In a competitive energy market, instead of, or in addition to, a centralized unit commitment, individual generation owners will make independent unit commitment decisions. They will seek to maximize their profits against the predicted market clearing price. Their unit commitment strategy will be expressed in their bids, so that they shut-down or start-up when the market price indicates such activity. In this chapter, we develop a unit commitment based price-taking (UCPT) bidding strategy with a simple price prediction mechanism and explore it using a market simulator. Simulation results show that an individual generator has higher profits with UCPT bidding than with simple price-taking bidding, and that the cost of supplying price-inelastic loads achieved by the market is lower when all generators use UCPT bidding. It appears that UCPT bidding gives results similar to those from a Lagrangian relaxation unit commitment (LRUC), without a fix-up step, and it has problems with convergence and feasibility similar to LRUC. We observe cyclic behavior in market prices with UCPT bidding, and we show that it depends on the price prediction mechanism. Alternative price prediction mechanisms can reduce cyclic behavior. Finally, we conceptually explore potential strategic behavior and market power arising from unit commitment constraints.KeywordsUnit CommitmentBidding StrategyMarket Clearing PriceFuture ProfitUnit Commitment ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An alternative framework to Lagrangian relaxation approach for job shop scheduling

Optimal management of sources, storage, and responsive loads in the energy network leads to improvement of economic and technical situation of the network. However, note that single-layer energy management requires to provide the network operator with more amounts of information. This results in the increase in the processing speed for the network operator. Also, coordination of sources, storage, and responsive loads in the form of an aggregator such as an energy hub can play an important role for them in promoting the economic situation and energy efficiency. So, this paper presents the participation of grid-connected energy hubs (EHs) in the day-ahead (DA) and real-time (RT) energy markets. Scheme has a two-stage optimization framework. In the first stage, participation of EHs in the hourly DA energy market is executed based on a two-layer energy management system that is modeled as bilevel problem. EH model in DA market is formulated in upper-level problem aimed at maximizing their profit subject to meeting the linearized constraints of the EH’s operation. Lower-level problem deals with the operation of energy networks to minimize cost of energy purchase from the upstream network constrained to linearized optimal power flow equations. The second stage of participation of EHs in a 5 min RT market has an objective function to minimize the unbalance between the EHs’ profits in the mentioned two markets. This stage is subject to the upper-level constraints and the lower-level formulation in the first stage. Robust optimization models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage to achieve an optimal robust solution. Benders decomposition algorithm solves bilevel formulation in two-stage scheme. Numerical results confirm capabilities of the scheme in enhancing operation status of the networks and economic situation of EHs. Hence, the Benders decomposition algorithm is able to extract the optimal solution in shorter computation time. Two-layer energy management needs shorter computation time than the single-layer management. Simultaneous modeling of two real-time and day-ahead energy markets reduces the unbalance cost between these markets. The proposed scheme can enhance the operating and economic indicators of the network by up to around 18% and 26% compared to power flow studies of energy networks.

In recent years, high penetration of variable generating sources, such as wind power, has challenged independent system operators (ISO) in keeping a cheap and reliable power system operation. Any deviation between expected and real wind production must be absorbed by the power system resources (reserves), which must be available and ready to be deployed in real time. To guarantee this resource availability, the system resources must be committed in advance, usually the day-ahead, by solving the so-called unit commitment (UC) problem. If the quantity of committed resources is extremely low, there will be devastating and costly consequences in the system, such as significant load shedding. On the other hand, if this quantity is extremely high, the system operation will be excessively expensive, mainly because facilities will not be fully exploited. This thesis proposes computationally efficient models for optimal day-ahead planning in (thermal) power systems to adequately face the stochastic nature of wind production in the real-time system operation. The models can support ISOs to face the new challenges in short-term planning as uncertainty increases dramatically due to the integration of variable generating resources. This thesis then tackles the UC problem in the following aspects: Power system representation: This thesis identifies drawbacks of the traditional energy-block scheduling approach, which make it unable to adequately prepare the power system to face deterministic and perfectly known events. To overcome those drawbacks, we propose the ramp-based scheduling approach that more accurately describes the system operation, thus better exploiting the system flexibility. UC computational performance: Developing more accurate models would be pointless if these models considerably increase the computational burden of the UC problem, which is already a complex integer and non-convex problem. We then devise simultaneously tight and compact formulations under the mixed-integer programming (MIP) approach. This simultaneous characteristic reinforces the convergence speed by reducing the search space (tightness) and simultaneously increasing the searching speed (compactness) with which solvers explore that reduced space. Uncertainty management in UC: By putting together the improvements in the previous two aspects, this thesis contributes to a better management of wind uncertainty in UC, even though these two aspects are in conflict and improving one often means harming the other. If compared with a traditional energy-block UC model under the stochastic (deterministic) paradigm, a stochastic (deterministic) ramp-based UC model: 1) leads to more economic operation, due to a better and more detailed system representation, while 2) being solved significantly faster, because the core of the model is built upon simultaneously tight and compact MIP formulations. To further improve the uncertainty management in the proposed ramp-based UC, we extend the formulation to a network-constrained UC with robust reserve modelling. Based on robust optimization insights, the UC solution guarantees feasibility for any realization of the uncertain wind production, within the considered uncertainty ranges. This final model remains as a pure linear MIP problem whose size does not depend on the uncertainty representation, thus avoiding the inherent computational complications of the stochastic and robust UCs commonly found in the literature.

In traditional electricity market unit commitment (UC) is done only for the reduction of operating cost. In the deregulated electricity market, for a secure and economical hourly generation schedule of the day ahead market, independent system operator (ISO) executes the security constrained unit commitment (SCUC) problem. The objective of the paper is to develop scheduling algorithm using hybrid particle swarm optimization (PSO) for electric generation that accounts directly for system security requirements. The proposed SCUC formulation includes constraints, such as hourly power demand, system reserves, ramp up/down limits, minimum ON/OFF duration limits. The innovation of our approach is to capture the system security requirements within a network flow sub-problem that is solved in conjunction with the unit commitment problem. To solve such SCUC model, a binary coded particle swarm optimization (BPSO) is used for unit commitment master problem and real coded particle swarm optimization (RPSO) is used for the economic dispatch sub-problem. The effectiveness of the suggested method to solve the SCUC problem is validated on different test systems.

An improved multi-area unit commitment and economic dispatch algorithm is proposed. The approach supplements the traditional dynamic programming unit commitment with heuristic adjustments to refine the commitment schedule in each area. A global economic dispatch scheme coordinates the transactions between areas. A new tie-line constraint checking is included which is simple and effective. The overall algorithm can be implemented on an IBM-PC, which can process a fairly large system in a reasonable period of time. Experimental results show the application potential of this method to general multi-area power generation scheduling problems.

Operational scheduling of intercity passenger transportation company participating in energy and reserve markets

This paper presents an optimization-based method to model the mid-term operation of a hydrothermal power system in a competitive market. Two market structures are examined: perfect competition and oligopoly comprising a dominant firm with a competitive fringe. In the latter, the dominant firm bidding strategy is determined by solving a profit maximization problem; forward contracts are also modeled. Producers submit offers to a day-ahead market, which is cleared by the system operator on a bid-cost minimization basis. A yearly planning horizon with hourly time intervals is adopted. A real size hydrothermal system, similar to the Greek Power System, is used for our tests. Hourly thermal unit commitment and start-up / shut-down decisions, market clearing prices, hydroplant generation / pumping and reservoir volumes, monthly price duration curves, ISO payment and thermal producer profits are among the results obtained.