Optimal investment portfolio management with hierarchical control for energy markets: An hierarchical control approach for smart grids (2016)
In the energy supply-demand chain, the connection between generators and load is generally performed by energy retailing companies. As such, they must fulfill the agreements and obligations signed with their customers by acquiring energy from generation companies and delivering it to the purchasers with quality and efficiency. In an ideal scenario, the retailer can maximize its operation’s returns by monitoring energy demand and prices, buying the energy at the lowest available price and providing the exact amount of energy a given client will consume. However, it is a fact that power system dynamics are too complex, especially in recent years, when the implementation of smart grid technologies (such as: renewable and green energy generation, policies to decrease CO2 emissions and small distributed generators, among others) has increased. Therefore, power system dynamics and the tools customarily used for power system planning and operation are becoming inefficient. As a result, it has become necessary to deal with several technical, financial and exogenous variables that may have different models and scales in order to improve energy retail performance; which, in turn, creates the need to propose new models, tools and management strategies to face the challenges of the emerging power system. Traditionally, energy retailers reduce operation uncertainties by making use of hedging strategies and energy portfolio diversification. These strategies require the trade of medium and long term energy assets. The use of assets such as energy derivatives or generation investments helps retailers reduce energy price uncertainties but, at the same time, introduces additional costs that must be considered in the retailer’s cash flow. In the short term operation and planning process, spot market is an additional instrument used to buy the energy required to meet inelastic demand or to sell energy excess. Short term portfolio is considered to be in charge of the market clearing process. Thus, a short term operation is the realization of the assumptions made within the management strategy design, and is reflected as positive or negative profits compared with previous cash flow expectations. Subsequently, tackling the issues of the inclusion of new retailer hedging strategy dynamics, it becomes increasingly relevant to update traditional management methodologies to meet future power system requirements and maximize operational returns. Making use of tools widely used for dynamic problems with high uncertainty levels, constraints and mixed time scales, this thesis proposes a new energy retailer management strategy. Considering system load dynamics, a methodology for an optimal generation plan expansion with technical constraints is used to design a generation matrix; with this result, a medium and long term generation investment plan is obtained, including an expected system operation schedule. The designed generation expansion plan provides technical operation parameters that allow for the safe inclusión of certain amounts of non conventional generation in the operation. Thereupon, future incomes and expenses related to planned generation are used to estimate energy prices while providing the expected energy retailer cash flow. Lastly, from the expected cash flow, a generation budget is provided to the short term portfolio. This budget is used as economic constraint in the short term optimization. In turn, this optimization is in charge of managing the joint operation of: generation plants, alternative generation and energy assets (spot market and forward agreements), to perform feasible market clearing. The market clearing process is made minimizing the operation costs with a dynamic optimization, the obtained short term returns become feedback for the estimated cash flow by measuring the real cash input into the economic balance, and using the profits to pay economic obligations of investments made. Interactively solving the entire problem in proper time scales, an optimal closed loop planing tool for energy retailers is provided. As planning tool, the proposed management strategy requires the use of forecasted data or scenarios that should be integrated with power system elements models, economic models and financial functions to reach a solution. All the aforementioned elements are included in dynamic optimization techniques used to solve the previously described energy retailing problem in a joint and coordinated way.