14,537 research outputs found
Operational planning and bidding for district heating systems with uncertain renewable energy production
In countries with an extended use of district heating (DH), the integrated
operation of DH and power systems can increase the flexibility of the power
system achieving a higher integration of renewable energy sources (RES). DH
operators can not only provide flexibility to the power system by acting on the
electricity market, but also profit from the situation to lower the overall
system cost. However, the operational planning and bidding includes several
uncertain components at the time of planning: electricity prices as well as
heat and power production from RES. In this publication, we propose a planning
method that supports DH operators by scheduling the production and creating
bids for the day-ahead and balancing electricity markets. The method is based
on stochastic programming and extends bidding strategies for virtual power
plants to the DH application. The uncertain factors are considered explicitly
through scenario generation. We apply our solution approach to a real case
study in Denmark and perform an extensive analysis of the production and
trading behaviour of the DH system. The analysis provides insights on how DH
system can provide regulating power as well as the impact of uncertainties and
renewable sources on the planning. Furthermore, the case study shows the
benefit in terms of cost reductions from considering a portfolio of units and
both markets to adapt to RES production and market states
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A decision model for natural oil buying policy under uncertainty
A manufacturer, in a fast moving consumer goods industry, buys Natural oils from a number of oil suppliers world-wide. The prices of these oils are the major raw material cost in producing the consumer goods, which are also sold world-wide. The volatility in the international prices of the Natural oils has signiĀÆcant impact on the planning and budgets decisions. Since the oils are bought and the ĀÆnished products are sold in markets throughout the world, the manufacturer is exposed to a variety of market uncertainties and the resulting risks. These uncertainties are the raw material prices, the demand and the therefore the selling prices for the finished goods- all of which influence the profitability of the manufacturing firm. The risks can be minimised by entering into futures contract of appropriate duration, that is, by following a schedule of "forward"' purchase of oil (with specific series of future delivery dates) with the oil suppliers. We formulate this problem as a two-stage Stochastic Program (SP) using the futures and the spot prices for the Natural oil. This SP model gives robust decisions that hedge against the uncertainties in the Natural oil prices and the demand for the finished products. The uncertainty in the oil prices and the demand are
modelled through a scenario generator. We have constructed a decision support system (DSS) that integrates the SP model, the scenario generator and the solution algorithm. This DSS also provides the decision maker a profile of the risk and return exposures for different policies
Multistage Stochastic Portfolio Optimisation in Deregulated Electricity Markets Using Linear Decision Rules
The deregulation of electricity markets increases the financial risk faced by retailers who procure electric energy on the spot market to meet their customersā electricity demand. To hedge against this exposure, retailers often hold a portfolio of electricity derivative contracts. In this paper, we propose a multistage stochastic mean-variance optimisation model for the management of such a portfolio. To reduce computational complexity, we perform two approximations: stage-aggregation and linear decision rules (LDR). The LDR approach consists of restricting the set of decision rules to those affine in the history of the random parameters. When applied to mean-variance optimisation models, it leads to convex quadratic programs. Since their size grows typically only polynomially with the number of periods, they can be efficiently solved. Our numerical experiments illustrate the value of adaptivity inherent in the LDR method and its potential for enabling scalability to problems with many periods.OR in energy, electricity portfolio management, stochastic programming, risk management, linear decision rules
Impact of Forecast Errors on Expansion Planning of Power Systems with a Renewables Target
This paper analyzes the impact of production forecast errors on the expansion
planning of a power system and investigates the influence of market design to
facilitate the integration of renewable generation. For this purpose, we
propose a stochastic programming modeling framework to determine the expansion
plan that minimizes system-wide investment and operating costs, while ensuring
a given share of renewable generation in the electricity supply. Unlike
existing ones, this framework includes both a day-ahead and a balancing market
so as to capture the impact of both production forecasts and the associated
prediction errors. Within this framework, we consider two paradigmatic market
designs that essentially differ in whether the day-ahead generation schedule
and the subsequent balancing re-dispatch are co-optimized or not. The main
features and results of the model set-ups are discussed using an illustrative
four-node example and a more realistic 24-node case study
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