10:30 AM - 11:00 AM
Assessing Electricity Market Integration: A Detailed Empirical Model of Trade Between a Thermal and a Hydro System
Electricity markets have been heavily influenced by the local availabilities of primary energy sources (hydro, coal, natural gas). Current transmission capacities and increased economic and environmental pressures create incentives to increase regional integration, possibly ending predominantly local electricity market regimes with "native load commitments" (i.e. the obligation to sell power locally at a regulated price, regardless of regional markets opportunities). This paper assesses the benefits of removing such native load commitments in a "hydro" jurisdiction trading with a "thermal" one. Our detailed hourly model, calibrated with real data (from the provinces of Ontario and Quebec, Canada), shows the price, consumption, emissions and welfare changes associated to fully integrating electricity markets, under transmission constraints.
11:00 AM - 11:30 AM
A Solver Manager for Energy Systems Planning within a Stochastic Optimization Framework
Within the EnRiMa project (Energy Efficiency and Risk Management in Public Buildings) a Decision Support System (DSS) is being developed aiming at supporting building operators on both operational and strategic decisions. Such DSS is composed by several integrated modules, which are in charge of specific tasks as a distributed system. Even though Stochastic Optimization has been used for decision making in energy markets, the EnRiMa approach represents a novelty as a tool for strategic decision making at the building level. The stochastic optimization problems stemmed from the EnRiMa project test sites have been modeled using scenario trees. In addition, another novel feature of the project, a so called dual-level scenario generator, allows to generate computationally affordable problem instances. This is crucial given that the strategic model has been formulated taking into account short-term building performance, which implies an increase on the problem complexity.
The strategic planning model provides optimal decisions considering all the scenarios, that is to say, not only average values of the random variables affecting the system (model parameters), but also their variability. Even though there is still residual uncertainty, such a solution is further more robust than the deterministic one. This is due to the fact that average values will probably never occur. Moreover, the optimal solution of a given deterministic problem (e.g. parameter average values, or more likely scenarios) could result on an infeasible solution for the eventual real scenario, having consequences on the objective (e.g. cost), or even leading to severe risk situations.
In this work we outline a strategic model for energy systems planning and its implementation within the EnRiMa DSS prototypes. The so-called Solver Manager module gathers the input from the rest of the modules through an interface, generates the problem instance, calls the optimization software, and delivers the solution eventually presented to the decision maker.
11:30 AM - 12:00 PM
Approximating Closed Loop Equilibria via Open Loop Equilibrium Models: An Application to Generation Expansion Planning in Liberalized Electricity Markets
In this work we propose a new methodology to approximate closed loop capacity equilibria using only open loop capacity equilibrium models and apply this approximation scheme to generation expansion planning in liberalized electricity markets.
In the closed loop model, generation companies choose capacities that maximize their individual profit in the first stage while the second stage represents the conjectured-price-response market equilibrium. In the open loop model, firms simultaneously choose capacities and quantities to maximize their individual profit, while each firm conjectures a price response to its output decisions. The closed loop equilibrium model is an equilibrium problem with equilibrium constraints (EPEC), which belongs to a class of problems that is very hard to solve. The open loop equilibrium model is much easier to solve, however, it is also less realistic in some situations. With the proposed approximation scheme, we are able to solve the open loop model reasonably well by smartly employing open loop models which reduces the computational time by two orders of magnitude. We achieve this by transforming the open loop equilibrium problem into an equivalent convex quadratic optimization problem which can be solved efficiently. The theoretical basis that sparked the idea of the approximation scheme is the comparison between open and closed loop equilibrium models from previous work of Wogrin et al. (2013), where we have found that the closed loop model yields Cournot capacities for any strategic behavior between perfect competition and the Cournot oligopoly, thereby extending the Kreps-Scheinkman-like results (1983).
The proposed methodology is applied to generation expansion planning in liberalized electricity markets. First, a small case study is presented in order to motivate the proposed approximation scheme and to obtain a first conclusion of when the quality of the results is high. In particular, we find that the approximation scheme works well when market behavior is closer to oligopoly than to perfect competition. These results are then confirmed in a large-scale case study for a multi-year, multi-load period and multi-technology numerical example.
12:00 PM - 12:30 PM
Transmission and Wind Investment in a Deregulated Electricity Industry
The transition to a more sustainable energy system requires investment in renewable energy technologies such as wind. Due to the dispersed nature of sites for wind farms, concomitant expansion of the transmission network is also necessary. While the two objectives could be reconciled within the auspices of a regulated welfare-maximising planner, recent restructuring of electricity industries has introduced a merchant model for transmission investment, which provides congestion rents from construction of a new line. Thus, the merchant’s incentives are different from those of producers carrying out investment in wind farms. In this paper, we analyse the interaction between the two conflicting objectives under various assumptions about the behaviour of the merchant and conjectural variations for the producers’ price response. Via a three-node illustrative example, we show that a dominant merchant typically builds less transmission capacity than one that cannot anticipate the decisions of producers behaving `a la Cournot. Furthermore, the impact of the setting on market outcomes is mixed with some nodal prices actually decreasing when the merchant is assumed to have dominance over the producers.