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In this paper, we explore the portfolio effect of a system consisting of a combined heat and power (CHP) plant and a wind farm. The goal is to increase the overall profit of the portfolio by reducing imbalances and, consequently, their implicit penalty in a two-price balancing market for electricity. We investigate two different operational strategies, which differ in whether the CHP plant and the wind farm are operated jointly or independently, and we evaluate their economic performance on a real case study based on a CHP-wind system located in the western part of Denmark. We present a comprehensive mathematical model for describing the different heat and power production units of the CHP plant and suggest different ways of determining its operation in a setup with two trading floors: a day-ahead market and a balancing market. We build a simulation framework that runs in a rolling-horizon fashion, so that forecasts for heat demand, wind power production, and market prices are updated at each iteration. We conclude that the portfolio strategy is the most profitable due to the two-price structure of the balancing market. This encourages producers to handle their imbalances outside the market.Index Terms-Balancing market, combined heat and power (CHP), electricity market, optimal portfolio operation, wind power.

We present two strategies for warmstarting primal-dual interior point methods for the homogeneous self-dual model when applied to mixed linear and quadratic conic optimization problems. Common to both strategies is their use of only the final (optimal) iterate of the initial problem and their negligible computational cost. This is a major advantage when compared to previously suggested strategies that require a pool of iterates from the solution process of the initial problem. Consequently our strategies are better suited for users who use optimization algorithms as black-box routines which usually only output the final solution. Our two strategies differ in that one assumes knowledge only of the final primal solution while the other assumes the availability of both primal and dual solutions. We analyze the strategies and deduce conditions under which they result in improved theoretical worst-case complexity. We present extensive computational results showing work reductions when warmstarting compared to coldstarting in the range 30%-75% depending on the problem class and magnitude of the problem perturbation. The computational experiments thus substantiate that the warmstarting strategies are useful in practice.

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