Constructing Bidding Curves for a CHP Producer in Day-ahead Electricity Markets

The operation of Combined Heat and Power (CHP) systems in liberalized electricity markets depends both on uncertain electricity prices and uncertain heat demand. In the future, uncertainty is going to increase due to the increased intermittent power induced by renewable energy sources. Therefore, the need for improved planning and bidding tools is highly important for CHP producers. This paper applies an optimal bidding model under the uncertainties of day-ahead market prices and the heat demand. The problem is formulated in a stochastic programming framework where future scenarios of the random variables are considered in order to handle the uncertainties. A case study is performed and conclusions are derived about the CHP operation and the need for heat storage.QC 20141208</p

CHP operation scheduling under uncertainty : Estimating the value of thermal energy storage

QC 20150507</p

Probabilistic day-ahead CHP operation scheduling

The production scheduling of combined heat and power plants is a challenging task. The need for simultaneous production of heat and power in combination with the technical constraints results in a problem with high complexity. Furthermore, the operation in the electricity markets environment means that every decision is made with unknown electricity prices for the produced electric energy. In order to compensate the increased risk of operating under such uncertain conditions, tools like stochastic programming have been developed. In this paper, the short-term operation scheduling model of a CHP system in the day-ahead electricity market is mathematically described and solved. The problem is formulated in a stochastic programming framework where the uncertain parameters of day-ahead electricity prices and the heat demand are incorporated into the problem in the form of scenarios. A case study is also performed with a CHP system operating in a district heating network and the value of heat storage capacity is estimated.QC 20160404</p

District heating system operation in power systems with high share of wind power

Abstract The integration of continuously varying and not easily predictable wind power generation is affecting the stability of the power system and leads to increasing demand for balancing services. In this study, a short-term operation model of a district heating system is proposed to optimally schedule the production of both heat and power in a system with high wind power penetration. The application of the model in a case study system shows the increased flexibility offered by the coordination of power generation, consumption and heat storage units which are available in district heating systems

EEM 2017 Forecast Competition : Wind power generation prediction using autoregressive models

Energy forecasting provides essential contribution tointegrate renewable energy sources into power systems. Today,renewable energy from wind power is one of the fastest growingmeans of power generation. As wind power forecast accuracygains growing significance, the number of models used forforecasting is increasing as well. In this paper, we propose anautoregressive (AR) model that can be used as a benchmarkmodel to validate and rank different forecasting models andtheir accuracy. The presented paper and research was developedwithin the scope of the European energy market (EEM) 2017wind power forecasting competition.QC 20170801</p

Constructing Offering Curves for a CSP Producer in Day-ahead Electricity Markets

In many countries, the installation and operation of concentrated solar power plants has been promoted with high feed-in tariffs and other incentives. However, as this technology is becoming more mature and the installation costs are being reduced, the incentives are minimized or totally abolished. Under these new economic conditions, there is an increased need for operation planning and power trading tools that will help the operators of such systems to make optimal decisions under the various uncertainties they face. This paper provides a model that can be used to derive the offering curves of a CSP producer in the day-ahead (spot) market. The model can also be used for the hourly short-term operation planning of the system. In order to tackle with the uncertainties of electricity prices and solar irradiance, the stochastic programming framework is used and a risk measure is incorporated into the model. A case study is conducted to show the applicability of the model.QC 20161220</p

Optimal Investment Planning of Bulk Energy Storage Systems

Many countries have the ambition to increase the share of renewable sources in electricity generation. However, continuously varying renewable sources, such as wind power or solar energy, require that the power system can manage the variability and uncertainty of the power generation. One solution to increase flexibility of the system is to use various forms of energy storage, which can provide flexibility to the system at different time ranges and smooth the effect of variability of the renewable generation. In this paper, we investigate three questions connected to investment planning of energy storage systems. First, how the existing flexibility in the system will affect the need for energy storage investments. Second, how presence of energy storage will affect renewable generation expansion and affect electricity prices. Third, who should be responsible for energy storage investments planning. This paper proposes to assess these questions through two different mathematical models. The first model is designed for centralized investment planning and the second model deals with a decentralized investment approach where a single independent profit maximizing utility is responsible for energy storage investments. The models have been applied in various case studies with different generation mixes and flexibility levels. The results show that energy storage system is beneficial for power system operation. However, additional regulation should be considered to achieve optimal investment and allocation of energy storage

Forecasting Balancing Market Prices Using Hidden Markov Models

This paper presents a Hidden Markov Model (HMM) based method to predict the prices and trading volumes in the electricity balancing markets. The HMM are quite powerful in modelling stochastic processes where the underlying dynamics are not apparent. The proposed method provides both one hour and 12-36 hour ahead forecasts. The first is mostly useful to wind/solar producers in order to compensate their production imbalances while the second is important when submitting the offers to the day ahead markets. The results are compared to the ones from Markov-autoregressive model.QC 20161017</p