Flexibility from Combined Heat and Power: A Techno-Economic Study for Fully Renewable Åland Islands

As energy systems globally are transitioning into renewable energy, simultaneous targets of high self-sufficiency have led to complex system design proposals. While conventional technology solutions would reduce the complexity in theory, limitations in the potential outcome may exist. To address this dilemma, the work quantified the systemic value provided by a conventional solution; biomass combined heat and power (CHP) production, in terms of economic feasibility, provided flexibility and energy self-sufficiency. The analysis focused on the renewable energy integration of the Åland Islands, where the synergetic island energy system is heavily increasing the wind power capacity. While considering local fuel resource availability, multiple alternative energy system scenarios were constructed. To evaluate the scenarios, the work developed and validated a combined dispatch and investment optimization model. The results showed that the studied conventional approaches limited the achievable self-sufficiency in the power sector (80.6%), however, considerably increasing the value from the present state (18.5%). Second, compared to previous studies, the results indicated a low value from biomass CHP in the wind-based energy system. Instead, the combination of high wind capacity and power-to-heat enabled the best economic feasibility and high self-sufficiency, which could be further improved by lower electricity taxation.

Hybrid quantum investment optimization with minimal holding period

In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal portfolios at each trading step using a quantum processor, and efficiently post-selecting to meet the minimal holding constraint. We found the optimal investment trajectory in a dataset of 50 assets spanning a 1 year trading period using the D-Wave 2000Q processor. Our method is remarkably efficient, and produces results much closer to the efficient frontier than typical portfolios. Moreover, we also show how our approach can easily produce trajectories adapted to different risk profiles, as typically offered in financial products. Our results are a clear example of how the combination of quantum and classical techniques can offer novel valuable tools to deal with real-life problems, beyond simple toy models, in current NISQ quantum processors.

Two-Stage Stochastic Model to Invest in Distributed Generation Considering the Long-Term Uncertainties

This paper used different risk management indicators applied to the investment optimization performed by consumers in Distributed Generation (DG). The objective function is the total cost incurred by the consumer including the energy and capacity payments, the savings, and the revenues from the installation of DG, alongside the operation and maintenance (O&M) and investment costs. Probability density function (PDF) was used to model the price volatility in the long-term. The mathematical model uses a two-stage stochastic approach: investment and operational stages. The investment decisions are included in the first stage and which do not change with the scenarios of the uncertainty. The operation variables are in the second stage and, therefore, take different values with every realization. Three risk indicators were used to assess the uncertainty risk: Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Expected Value (EV). The results showed the importance of migration from deterministic models to stochastic ones and, most importantly, the understanding of the ramifications of every risk indicator.

Enhancement of a District Heating Substation as Part of a Low-Investment Optimization Strategy for District Heating Systems

In an ongoing project, low-investment measures for the optimization of district heating systems are analyzed. The optimization strategies are collected in a catalog, which is the core of a guideline. The application of this guideline is demonstrated using two concrete district heating networks as examples. In this study, the improvement of an analog controlled district heating substation by an electronic controller is investigated. High supply temperatures and heat losses are often a challenge in district heating networks. The district heating substations have a major influence on the network return temperatures. The comparison of the two substation setups with analog and electronic controllers is carried out by laboratory measurement. It can be shown that the return temperatures can be reduced by an average of 20 K in winter and transition, as well as 16 K in summer. The district heating network losses are calculated for one of both specific district heating networks. They are calculated from the ratio of network losses to generated energy. The generated energy is the sum of network losses and consumer demand. The thermal losses of the network can be reduced by 3%. The volume flow in the heating network can be reduced to a quarter. Therefore, the pumping energy requirement drops sharply since these changes cubically affect the volume flow.

Sweet cherry pruning fundamentals.

This chapter focuses on the fundamentals of pruning sweet cherry orchards. The fundamentals of training and pruning contemporary sweet cherry orchards usually incorporate plant materials or techniques that promote: early fruiting for a more rapid return on investment; optimization of light interception and distribution, with minimal intra-canopy shading; greater precision in, and/or simplification of, canopy development for balancing yields with fruit size and quality, as well as ease of teaching to less experienced labor forces; and systematic processes for annual renewal of fruiting wood to maintain consistent yields of high fruit quality as trees age.