Birger Mo

Integrated risk management of hydro power scheduling and contract management

The paper describes the implementation of a new integrated tool for risk management in hydropower systems. Earlier practice in Scandinavia has been to separate operations scheduling and contract management. In the present approach operation scheduling and hedging by future contacts are integrated in one model. The risk level is controlled by setting revenue targets. Revenues below target are penalized; this implicitly defines a revenue utility function to reduce risk. The possibility of dynamically changing the future contract portfolio is now represented. The resulting large stochastic dynamic optimization problem is solved using a combination of stochastic dynamic programming and stochastic dual dynamic programming. Simulations for a test case show that the profit in the lower range is considerably improved with the new tool. The approach can be useful for hydropower companies that face price risks in addition to the inflow uncertainty, as is the case in a deregulated system.

Long- and Medium-term Operations Planning and Stochastic Modelling in Hydro-dominated Power Systems Based on Stochastic Dual Dynamic Programming

This chapter reviews how stochastic dual dynamic programming (SDDP) has been applied to hydropower scheduling in the Nordic countries. The SDDP method, developed in Brazil, makes it possible to optimize multi-reservoir hydro systems with a detailed representation. Two applications are described: (1) A model intended for the system of a single power company, with the power price as an exogenous stochastic variable. In this case the standard SDDP algorithm has been extended; it is combined with ordinary stochastic dynamic programming. (2) A global model for a large system (possibly many countries) where the power price is an internal (endogenous) variable. The main focus is on (1). The modelling of the stochastic variables is discussed. Setting up proper stochastic models for inflow and price is quite a challenge, especially in the case of (2) above. This is an area where further work would be useful. Long computing time may in some cases be a consideration. In particular, the local model has been used by utilities with good results.

Assessing climate change impacts on the Iberian power system using a coupled water-power model

Climate change is expected to have a negative impact on the power system of the Iberian Peninsula; changes in river runoff are expected to reduce hydropower generation, while higher temperatures are expected to increase summer electricity demand, when water resources are already limited. However, these impacts have not yet been evaluated at the peninsular level. We coupled a hydrological model with a power market model to study three impacts of climate change on the current Iberian power system: changes in hydropower production caused by changes in precipitation and temperature, changes in temporal patterns of electricity demand caused by temperature changes, and changes in irrigation water use caused by temperature and precipitation changes. A stochastic dynamic programming approach was used to develop operating rules for the integrated system given hydrological uncertainty. We found that changes in precipitation will reduce runoff, decrease hydropower production (with accompanying increases in thermal generation), and increase irrigation water use, while higher temperatures will shift power demand from winter to summer months. The combined impact of these effects will generally make it more challenging to balance agricultural, power, and environmental objectives in the operation of Iberian reservoirs, though some impacts could be mitigated by better alignment between temporal patterns of irrigation and power demands.

Optimisation of hydropower operation in a liberalised market with focus on price modelling

This paper describes the structure and identification of a price model that is used in stochastic optimization of hydro operation and flexible contracts. The price model must be simple in order to be applicable in a stochastic optimization framework and the model should incorporate as much of the statistics of the price process as possible. Modelling of extremes is an important factor for the simulation capabilities of the optimisation models. The paper shows examples of simulated optimal operation of hydropower plants with the new price model. The paper also shows how the price model is used in a model that integrates hydro operation and financial hedging. In the forward market, prices of contracts with delivery several years ahead vary from one week to the next. In order to model this long-term uncertainty we have amended our spot price model.

Long-term hydro-thermal scheduling including network constraints

This paper presents a method for treating transmission network bottlenecks in a stochastic hydro-thermal scheduling model. The model is designed for long- and medium-term scheduling of hydro-thermal power system operation, where decisions are made for aggregated regional subsystems or areas. The aggregate area representation allows simulation of large hydro systems with a relatively high degree of detail, thus making the model well suited for comprehensive studies on a national and international scale.

Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

This paper describes a method for optimal scheduling of hydropower systems for a profit maximizing, price-taking, and risk neutral producer selling energy, and capacity to separate and sequentially cleared markets. The method is based on a combination of stochastic dynamic programming (SDP) and stochastic dual dynamic programming (SDDP), and treats inflow to reservoirs and prices for energy and capacity as stochastic variables. The proposed method is applied in a case study for a Norwegian watercourse, quantifying the expected changes in schedules, and water values when going from an energy-only market to a joint treatment of energy and reserve capacity markets.

Optimization of Multipurpose Reservoir Systems Using Power Market Models

AbstractHydroeconomic models have been used to determine policies for efficient allocation of scarce water resources. Hydropower benefits are typically represented through exogenous electricity prices, but these do not consider the effect that the power market can have on the hydropower release policy and vice versa. To improve the representation of hydropower benefits in hydroeconomic models, an application of stochastic dynamic programming, known as the water value method, was used to maximize irrigation benefits while minimizing the costs of power generation within a power market. The method yields optimal operation rules that maximize current and expected future benefits as a function of reservoir level, week of the year, and inflow state. The method was tested on the Iberian Peninsula and performed better than traditional approaches that use exogenous prices: resulting operation rules were more realistic and sensitive to hydrological variability. Internally calculated hydropower prices provided bette...

Handling balancing power in a power market with a large share of hydropower

This paper presents a model well suited for analysis of power systems with a large share of new renewable in combination with hydropower and pumped-storage hydro. The model is designed for a detailed hydropower representation and can be used with hourly time resolution. The model solves the detailed dispatch of hydropower in two steps. First it uses a heuristic model for weekly decisions where the goal is to find the target for end reservoir levels. Then the problem is re-solved as a formal optimization problem where the goal is to fully utilize the flexibility in the hydropower system within the week.

Hydrothermal scheduling in Norway using stochastic dual dynamic programming; a large-scale case study

We test the stochastic dual dynamic programming (SDDP) approach on a system an order of magnitude larger than previously published studies. The analysis shows that the SDDP-approach can be applied to very large system sizes to solve the hydropower scheduling problem through formal optimisation and obtain individual decision variables for every reservoir. However, this can be very time-consuming compared to other existing models based on other principles. The results from our SDDP-based model compare favorably to an aggregation-disaggregation model which is in operational use in the power market when using statistical inflow series as input to the models.

Co-optimizing sales of energy and capacity in a hydropower scheduling model

This paper describes a model for optimal scheduling of hydroelectric systems for a price-taking producer selling energy and capacity to separate markets. The model is based on a combination of stochastic dynamic programming (SDP) and stochastic dual dynamic programming (SDDP), and treats inflow to reservoirs and energy prices as stochastic variables. It allows sales of capacity at a deterministic sequence of capacity reserve prices. Thus, the sales of energy and capacity is co-optimized within the SDDP framework. The presented model is tested on a Norwegian watercourse where the producer sells energy to the day-ahead market and capacity to the primary reserve markets. When adding the possibility to sell capacity in the model, the results show that less water is used during winter and more during summer/autumn in order to sell capacity.