Session 12: Power Generation Strategic Planning (2)
Data-collecting Information System – Basis for Managing, Planning and Monitoring Operations of the HEP Proizvodnja d.o.o.’s Production Capacities
1HEP Proizvodnja d.o.o., Croatia; 2HEPd.d., Croatia
Data on indicators of production units are the base for planning, managing and business decisions making of all energy companies including the HEP Proizvodnja. Nowadays this can be achieved only with an appropriate integrated information system, which did not exist so far. Therefore, the HEP Proizvodnja decided to develop and implement such system independently. The work presents past accomplishments, functions and capabilities of the system as well as further planned development of the system.
Model of Short Term Hydro Power Plant Scheduling in Competitive Environment
The traditional role of hydro power plants was to reduce the total generation cost of the entire power system. Becoming independent and market oriented subjects, the value of water and positioning of hydro power plants on the energy market requires new scheduling solutions. This paper proposes a new model of the power system scheduling where hydro power plants have an active role as market participants. The idea is based on the existing model it observes only the behavior of thermal power plants. Case study includes several scenarios which demonstrate the optimal utilization of water resources with the goal of maximizing the profit of each hydro power plant in the cascade system. Each hydro power plant is considered to be a single independent entity in the market and is modeled as such in the proposed solution.
A Decision Support System for Hydro Power Plants in Markets for Energy and Ancillary Services
1HEP Proizvodnja d.o.o., PP HE Sjever, Varaždin, Croatia; 2HEP Proizvodnja d.o.o., PP HE Sjever, Varaždin, Croatia; 3Faculty of Organization and Informatics, Varaždin, Croatia
This paper analyses solutions for optimal bidding for hydro units operating in simultaneous markets for energy and ancillary services and decision making process for plant refurbishment and generating capacity upgrade. Methodology based on the Decision Theory will be applied to identify the optimal solutions which minimize the expected costs and the related risks. The proposed methodology will be demonstrated for a real hydropower plant in Croatia (Varazdin HPP) which will be refurbished.
The State-of-the-Art of the Short Term Hydro Power Planning with Large Amount of Wind Power in the System
The amount of wind power is growing significantly in the world. Large scale introduction of wind power in the power system will increase the need for improved short term planning models of hydro power, because additional variations are introduced in the system. This huge amount of uncertainties in the power system will cause changes in the power market and there will be a value of advanced planning techniques, that will allow more flexibility in hydropower generation by taking into account stochastic nature of spot and regulating markets, water inflow, future water value and so on. The application of multi-stage stochastic optimization in the planning of the daily production of hydro power is not wholly discovered and requires further research. The complexity of the mathematical programming of the short term hydro power production including several type of uncertainty, while keeping the problem size solvable, challenges the power system researchers. This paper overviews the literature in the field of short term hydro power planning in power systems with large amount of wind power.
A Particle Swarm Optimization Method for Power Bidding Coal Units in Competitive Auctions
1Kadir Has University, Fatih, Istanbul, Turkey; 2Auburn University, Auburn, AL, USA
In most competitive markets, power firms bid daily into the day-ahead power market. The auction mechanism and competition determine the equilibrium price and quantity for each hour. The profit maximization for price-taker units under price uncertainty and blind auction rules requires careful preparations of bidding offers. In this paper, we develop a nonlinear method and a particle swarm method to determine a bidding offer for a price-taker coal unit under price uncertainty. We show the computational limitations for nonlinear programming by comparing the solutions of both methods.
Medium-Term Unit Commitment in a Pool Market
1University of Thessaly, Greece; 2Aristotle University of Thessaloniki, Greece
We consider a mandatory pool, based to the one established in the Greek electricity market, in which the unit commitment and the scheduling of energy and reserves are the solution of Day-Ahead Scheduling (DAS), an optimization problem that is solved daily and aims to minimize the system cost for the next day. The single-day horizon of DAS may be rather short for capturing the effects of the long start-up times and large commitment costs of slow-start lignite units; hence, the DAS solution may be myopic, resulting in higher total costs in the long-run. To tackle this problem, the Greek market uses a heuristic approach, in which the units’ shut-down costs are replaced by their start-up costs and the start-up costs are suppressed; this facilitates the start-up and discourages the shutdown of slow-start units. To address and evaluate the “myopic solution” issue of DAS more rigorously, we extend the unit commitment problem to a longer horizon of several days, and keep only the solution for the next day as binding (rolling horizon). We call the resulting approach Medium-Term Unit Commitment (MTUC). We compare the long-run average performance of the MTUC output for different horizon lengths (2, 4 and 7 days) to that of the heuristic DAS approach used in the Greek market. The results show that MTUC brings in a small reduction in the total system cost.
The Mixed-Integer Linear Optimization Model of Virtual Power Plant Operation
University of Zagreb, Croatia
The concept of virtual power plant is developed for two prime reasons. First, to diversify the risk of not meeting the long-term electricity delivery contracts, and secondly to achieve better results on the electricity market. This paper regards the case in which wind power plant and solar power plant are joint together with a conventional gas power plant to act on the electricity market as a single agent. The problem is formulated as the mixed-integer linear programming model which incorporates long-term bilateral contracts with weekly forecasted hourly market prices. The aim of the optimization is to maximize the profit of virtual power plant. The efficiency of the proposed model is rendered through two case studies and detailed analysis is provided.