Sijie Chen

Commercial greenhouses are becoming significant, controllable, and weather-dependent electricity loads in regions pursuing controlled environment agriculture (CEA). Their electrical demands—such as supplemental lighting, heating, ventilation/cooling, irrigation pumps, and CO₂ enrichment—exhibit substantial intra-day flexibility. This flexibility stems from the fact that plant productivity depends on time-integrated agronomic variables (e.g., Daily Light Integral, accumulated thermal time, and mean vapor pressure deficit) rather than instantaneous environmental setpoints. However, existing studies have predominantly focused on greenhouse thermal modeling and energy conservation, while decision-making models that integrate crop physiological characteristics into day-ahead (DA) market bidding remain limited. To bridge this gap, this paper develops a scenario-based stochastic DA bidding framework for a load aggregator, representing multiple smart greenhouses in a wholesale electricity market. In the DA stage, the aggregator submits hourly demand–price bidding curves based on price-conditional schedules of heating and lighting demand while satisfying coupled thermal–photon balance constraints. Fifty representative scenarios generated from 2023 to 2024 historical price and weather data through cGAN-based scenario generation and K-means scenario reduction are used for stochastic bid construction and feasibility analysis. A separate 30-day historical dataset from January 2025 is used for benchmark comparison. The aggregator portfolio consists of 100 greenhouses divided into five LAI-based crop groups, with 20 greenhouses in each group. Relative to the 15–25 °C trapezoidal temperature baseline, which is adopted as the primary practical benchmark, the proposed strategy reduces the mean daily electricity procurement cost by 15.80%. A reduction of 18.89% is also observed relative to the rigid 20 °C thermostat case, which is retained as a capacity-intensive reference. These results represent simulation-based operating-cost comparisons under a common equipment configuration and do not include equipment capital costs.

Integration of distributed energy resources, such as photovoltaics, has expanded rapidly within power distribution networks in recent years. Existing management architectures face great challenges in balancing data security and communication efficiency. To address this issue, the paper proposes a cloud-edge collaborative framework that caters to managing multiple distributed energy resources. Firstly, a two-stage structural design method is introduced to determine the optimal configuration of edge nodes for variable reduction. Secondly, a credit-based data interaction scheme considering inherent uncertainty of distributed energy resources is proposed to ensure trustworthy cloud-edge collaborative optimizations. The above work is expected to facilitate the in-depth application of cloud-edge collaboration in the energy scheduling field.

An integrated zero-carbon power plant aggregates uncontrollable green energy, adjustable load, and storage energy resources into an entity in a grid-friendly manner. Integrated zero-carbon power plants have a strong demand response potential that needs further study. However, existing studies ignore the green value of renewable energy in power plants when participating in demand response programs. This paper proposed a mathematical model to optimize the operation of an integrated zero-carbon power plant considering the green value. A demand response mechanism is proposed for the independent system operator and the integrated zero-carbon power plants. The Stackelberg gaming process among these entities and an algorithm based on dichotomy are studied to find the demand response equilibrium. Case studies verify that the mechanism activates the potential of the integrated zero-carbon power plant to realize the load reduction target.

Concept drift means the statistical properties of the variable that a predictor is predicting change over time in unforeseen ways. Existing research solves concept drift in the locational marginal price (LMP) prediction process by updating predictors in online approaches. However, new data is indiscriminately utilized to update predictors in these methods. The new property changes can not be accurately captured when concept drift occurs. This paper proposes a stacking framework for online LMP prediction considering the concept drift phenomenon. Long short-term memory networks and graph attention networks are selected as the base predictors to capture the spatio-temporal dependencies in LMPs. When concept drift occurs, data with drift selected by the adaptive windowing algorithm is used to update the stacked predictor. Numerical results based on real data from Australian Energy Market Operator and Midcontinent Independent System Operator validate the effectiveness of the proposed framework. The comparative experiments prove that attempts to change or simplify the proposed framework can undermine prediction accuracy

Rapid growth in the number of electric vehicles (EVs) calls for research on traffic equilibrium in urban transportation networks (UTNs). As a result of the interaction of EVs and fast charging stations (FCSs), traffic flow is influenced by EV drivers’ charging preferences. However, little research investigates the impact of heterogeneous charging preferences across EV drivers on the traffic flow at traffic equilibrium. This paper establishes a traffic model where the heterogeneity across EV drivers is reflected in the monetary value of time and sensitivity to electricity prices. Based on the traffic model, we propose a Stackelberg game to model the interaction of EVs and FCSs while ensuring the user equilibrium of UTN. The game features a hierarchical structure. At the higher level, FCSs play a non-cooperative game by strategically setting electricity prices with imperfect information of other FCSs. At the lower level, EVs play an evolutionary game to charge at FCSs with the lowest cost. An adaptive step control method is designed to prevent potential oscillations near the Nash equilibrium. Numerical results show that the difference in EV flow on the road can reach 18.42% without considering heterogeneity and considering heterogeneity. The proposed framework can illustrate how the heterogeneity across EVs influences the formation of traffic flow in traffic equilibrium calculation.

Due to the missing money problem, it is a challenging task to maintain generation capacity adequacy with increasing shares of zero-marginal-cost renewable energy resources. Multiple capacity adequacy mechanisms (CAMs), such as scarcity pricing, capacity payment, capacity market, and reliability option, have been proposed and implemented, each having its own pros and cons. Here, we propose a hybrid CAM called optional option. The hybrid CAM allows scarcity pricing and reliability option to co-exist. Market participants, with diverse degrees of risk aversion and diverse forecasts of future duration of scarcity, can self-select the CAM best suited to themselves. We also present a method to efficiently derive the generation mix equilibrium under the CAM, by using the theory that the profits of all generator types approach zero in the long-term equilibrium. Simulation results show that the proposed CAM enables Pareto improvement by letting market participants be better off than being restricted to only one CAM. The results also show the effectiveness of the algorithm to calculate the generation mix equilibrium.

It is always desired for optimizing energy trading to disable manipulation and preserve individual privacy. These two features become increasingly appealing for an energy market where interest parties do not mutually trust each other, such as peer-to-peer energy trading. Traditional centralized or hierarchical optimization schemes are vulnerable to an untrusted coordinator who may dishonestly broadcast coordination results or be curious about individual privacy. Recent blockchain-based optimization schemes resist dishonesty but increase the risk of privacy leakage. This paper proposes a privacy-preserving blockchain-based method to optimize energy trading. In the proposed method, participants submit encrypted bids/offers based on a bid/offer encryption algorithm to preserve their privacy. A privacy-preserving-Byzantine-fault-tolerance-based coordination algorithm is proposed to ensure the correctness of trading results with considering dishonesty. Numerical results in a peer-to-peer energy trading case demonstrate the performance of our method on convergence, resisting dishonesty, preserving privacy, and scalability.