Shape-constrained Estimation of Value Functions

We present a fully nonparametric method to estimate the value function, via simulation, in the context of expected infinite-horizon discounted rewards for Markov chains. Estimating such value functions plays an important role in approximate dynamic programming and applied probability in general. We incorporate "soft information" into the estimation algorithm, such as knowledge of convexity, monotonicity, or Lipchitz constants. In the presence of such information, a nonparametric estimator for the value function can be computed that is provably consistent as the simulated time horizon tends to infinity. As an application, we implement our method on price tolling agreement contracts in energy markets

Seasonal hydrogen storage decisions under constrained electricity distribution capacity

The transition to renewable energy systems causes increased decentralization of the energy supply. Solar parks are built to increase renewable energy penetration and to supply local communities that become increasingly self-sufficient. These parks are generally installed in rural areas where electricity grid distribution capacity is limited. This causes the produced energy to create grid congestion. Temporary storage can be a solution. In addition to batteries, which are most suitable for intraday storage, hydrogen provides a long-term storage option and can be used to overcome seasonal mismatches in supply and demand. In this paper, we examine the operational decisions related to storing energy using hydrogen, and buying from or selling to the grid considering grid capacity limitations. We model the problem as a Markov decision process taking into account seasonal production and demand patterns, uncertain solar energy generation, and local electricity prices. We show that ignoring seasonal demand and production patterns is suboptimal. In addition, we show that the introduction of a hydrogen storage facility for a solar farm in rural areas may lead to positive profits, whereas this is loss-making without storage facilities. In a sensitivity analysis, we show that only if distribution capacity is too small, hydrogen storage does not lead to profits and reduced congestion at the cable connection. When the distribution capacity is constrained, a higher storage capacity leads to more buying-related actions from the electricity grid to prevent future shortages and to exploit price differences. This leads to more congestion at the connected cable and is an important insight for policy-makers and net-operators

Efficient Operation of Modular Grid-Connected Battery Inverters for RES Integration

Grid-connected battery storage systems on megawatt-scale play an important role for the integration of renewable energies into electricity markets and grids. In reality, these systems consist of several batteries and inverters, which have a lower energy conversion efficiency in partial load operation. In renewable energy sources (RES) applications, however, battery systems are often operated at low power. The modularity of grid-connected battery storage systems thus allows improving system efficiency during operation. This contribution aims at quantifying the effect of segmenting the system into multiple battery-inverter subsystems on reducing operating losses. The analysis is based on a mixed-integer linear program that determines the system operation by minimizing operating losses. The analysis shows that systems with high modularity can meet a given schedule with lower losses. Increasing modularity from one to 32 subsystems can reduce operating losses by almost 40%. As the number of subsystems increases, the benefit of higher efficiency decreases. The resulting state of charge (SOC) pattern of the batteries is similar for the investigated systems, while the average SOC value is higher in highly modular systems

Efficient Energy Optimization for Smart Grid and Smart Community

The electric power industry has undergone significant changes in response to the environmental concerns during the past decades. Nowadays, due to the integration of different distributed energy systems in the smart grid, the balancing between power generation and load demand becomes a critical problem. Specifically, due to the intermittent nature of renewable energy sources (RESs) , power system optimization becomes significantly complicated. Due to the uncertain nature of RESs, the system may fail to ensure the power quality which may cause increased operating costs for committing costly reserve units or penalty costs for curtailing load demands. This dissertation presents three projects to study the optimization and control for smart grid and smart community. First, optimal operation of battery energy storage system (BESS) in grid-connected microgrid is studied. Near optimal operation/allocation of the BESS is investigated with the consideration of battery lifetime characteristics. Approximate dynamic programming (ADP) is proposed to solve optimal control policy for time-dependent and finite-horizon BESS problems and performance comparison is done with classical dynamic programming approach. The results show that the ADP can optimize the system operation under different scenarios to maximize the total system revenue. Second, optimal operation of the BESS in islanded microgrid is also studied. Specifically, a new islanded microgrid model is formulated based on Markov decision process. A computationally efficient ADP approach is proposed to solve this energy optimization problem, and achieve near minimum operational cost efficiently. Simulation results show that the proposed ADP can achieve 100% and at least 98% of optimality for deterministic and stochastic case studies, respectively. The performance of the proposed ADP approach also achieved 18:69 times faster response than that of the traditional DP approach for 0:5 million of data samples. Third, a demand side management technique is proposed for the optimization of residential demands with financial incentives. A new design of comfort indicator is proposed considering both thermal and other electric appliances based on consumers’ comfort level. The proposed approach is compared with two existing demand response approaches for both 10-houses and 100-houses simulation studies. For both cases, the proposed approach outperformed the existing approaches in terms of reward incentives and comfort levels

Advances in Energy System Optimization

The papers presented in this open access book address diverse challenges in decarbonizing energy systems, ranging from operational to investment planning problems, from market economics to technical and environmental considerations, from distribution grids to transmission grids, and from theoretical considerations to data provision concerns and applied case studies. While most papers have a clear methodological focus, they address policy-relevant questions at the same time. The target audience therefore includes academics and experts in industry as well as policy makers, who are interested in state-of-the-art quantitative modelling of policy relevant problems in energy systems. The 2nd International Symposium on Energy System Optimization (ISESO 2018) was held at the Karlsruhe Institute of Technology (KIT) under the symposium theme “Bridging the Gap Between Mathematical Modelling and Policy Support” on October 10th and 11th 2018. ISESO 2018 was organized by the KIT, the Heidelberg Institute for Theoretical Studies (HITS), the Heidelberg University, the German Aerospace Center and the University of Stuttgart