Value-oriented Renewable Energy Forecasting for Coordinated Energy Dispatch Problems at Two Stages

Energy forecasting is deemed an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time (referred to as the 'predict, then optimize' paradigm). However, forecast models are often developed via optimizing statistical scores while overlooking the value of the forecasts in operation. In this paper, we design a value-oriented point forecasting approach for energy dispatch problems with renewable energy sources (RESs). At the training phase, this approach incorporates forecasting with day-ahead/real-time operations for power systems, thereby achieving reduced operation costs of the two stages. To this end, we formulate the forecast model parameter estimation as a bilevel program at the training phase, where the lower level solves the day-ahead and real-time energy dispatch problems, with the forecasts as parameters; the optimal solutions of the lower level are then returned to the upper level, which optimizes the model parameters given the contextual information and minimizes the expected operation cost of the two stages. Under mild assumptions, we propose a novel iterative solution strategy for this bilevel program. Under such an iterative scheme, we show that the upper level objective is locally linear regarding the forecast model output, and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used point forecasting methods, the forecasts obtained by the proposed approach result in lower operation costs in the subsequent energy dispatch problems. Meanwhile, the proposed approach is more computationally efficient than traditional two-stage stochastic program.Comment: submitted to European Journal of Operational Researc

The Design of By-product Hydrogen Supply Chain Considering Large-scale Storage and Chemical Plants: A Game Theory Perspective

Hydrogen, an essential resource in the decarbonized economy, is commonly produced as a by-product of chemical plants. To promote the use of by-product hydrogen, this paper proposes a supply chain model among chemical plants, hydrogen-storage salt caverns, and end users, considering time-of-use (TOU) hydrogen price, coalition strategies of suppliers, and road transportation of liquefied and compressed hydrogen. The transport route planning problem among multiple chemical plants is modeled through a cooperative game, while the hydrogen market among the salt cavern and chemical plants is modeled through a Stackelberg game. The equilibrium of the supply chain model gives the transportation and trading strategies of individual stakeholders. Simulation results demonstrate that the proposed method can provide useful insights on by-product hydrogen market design and analysis

Convergence Analysis of Dual Decomposition Algorithm in Distributed Optimization:Asynchrony and Inexactness

Dual decomposition is widely utilized in the distributed optimization of multi-agent systems. In practice, the dual decomposition algorithm is desired to admit an asynchronous implementation due to imperfect communication, such as time delay and packet drop. In addition, computational errors also exist when the individual agents solve their own subproblems. In this paper, we analyze the convergence of the dual decomposition algorithm in the distributed optimization when both the communication asynchrony and the subproblem solution inexactness exist. We find that the interaction between asynchrony and inexactness slows down the convergence rate from &lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\mathcal {O} (1 / k)$&lt;/tex-math&gt;&lt;/inline-formula&gt; to &lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\mathcal {O} (1 / \sqrt{k})$&lt;/tex-math&gt;&lt;/inline-formula&gt;. Specifically, with a constant step size, the value of the objective function converges to a neighborhood of the optimal value, and the solution converges to a neighborhood of the optimal solution. Moreover, the violation of the constraints diminishes in &lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\mathcal {O} (1 / \sqrt{k})$&lt;/tex-math&gt;&lt;/inline-formula&gt;. Our result generalizes and unifies the existing ones that only consider either asynchrony or inexactness. Finally, numerical simulations validate the theoretical results.</p

Distributed Generalized Nash Equilibrium Seeking for Energy Sharing Games

With the proliferation of distributed generators and energy storage systems, traditional passive consumers in power systems have been gradually evolving into the so-called "prosumers", i.e., proactive consumers, which can both produce and consume power. To encourage energy exchange among prosumers, energy sharing is increasingly adopted, which is usually formulated as a generalized Nash game (GNG). In this paper, a distributed approach is proposed to seek the Generalized Nash equilibrium (GNE) of the energy sharing game. To this end, we convert the GNG into an equivalent optimization problem. A Krasnosel'ski{\v{i}}-Mann iteration type algorithm is thereby devised to solve the problem and consequently find the GNE in a distributed manner. The convergence of the proposed algorithm is proved rigorously based on the nonexpansive operator theory. The performance of the algorithm is validated by experiments with three prosumers, and the scalability is tested by simulations using 123 prosumers

Overview of Data-driven Power Flow Linearization

The accuracy limitation of physics-driven power flow linearization approaches and the widespread deployment of advanced metering infrastructure render data-driven power flow linearization (DPFL) methods a valuable alternative. While DPFL is still an emerging research topic, substantial studies have already been carried out in this area. However, a comprehensive overview and comparison of the available DPFL approaches are missing in the existing literature. This paper intends to close this gap and, therefore, provides a narrative overview of the current DPFL research. Both the challenges (including data-related and power-system-related issues) and methodologies (namely regression-based and tailored approaches) in DPFL studies are surveyed in this paper; numerous future research directions of DPFL analysis are discussed and summarized as well