Chuanwen Jiang

Integrated energy system (IES) is an effective way to realize the efficient utilization of energy. Under the deregulated electricity market, IES operator gains profits by providing customers with energy service, including electricity, heat or cooling energy. With the deepening of market reform, higher penetration rate of renewable energy, economic risks embed in the IES. Based on this, an optimal scheduling model of regional IES considering uncertainties is proposed, aiming at maximizing the profits. Scenario analysis method has been adopted to model the uncertainties: Markov-Chain-Monte-Carlo (MCMC) sampling method, which has a better performance in fitting the probability distribution, is utilized to generate scenarios; K-means clustering method is applied to narrow down the sampling sets. By replacing the parameters in the deterministic model with the sampling sets, a series of optimal results can be achieved. The case study shows that the cooling storage tank can improve the economic benefits about 4.97% by converting electricity to cooling energy at lower price period and releasing energy at peak hours. Besides, through the proposed optimization model, operators can have a straight understanding of the venture brought by the uncertainties and a more reliable scheduling result is formed for reference.

A virtual power plant is proposed to aggregate various distributed renewable resources with controllable resources to overcome the uncertainty and volatility of the renewables so as to improve market involvement. As the virtual power plant capacity becomes remarkable, it behaves as a strategic price maker rather than price taker in the market for higher profit. In this work, a two-stage bi-level bidding and scheduling model is proposed to study the virtual power plant strategic behaviors as a price maker. A mathematical problem with an equilibrium constraints-based method is applied to solve the problem by transforming the two level problem into a single level multi-integer linear problem. Considering the deficiency of computational burden and implausible assumptions of conventional stochastic optimization, we introduce interval numbers to represent the predicted output of uncertainty resources in a real-time stage. The pessimism degree-based method is utilized to order the preferences of profit intervals and tradeoff between expected profit and uncertainty. An imbalance cost mitigation mechanism is proposed in this pessimism degree-based interval optimization manner. Results show that the bidding price directly affects the cleared day ahead of the locational marginal price for higher profit. Interior conventional generators, energy storage and interruptible loads are comprehensively optimized to cover potential power shortage or profit from market. Moreover, controllable resources can decrease or even wipe out the uncertainty through the imbalance cost mitigation mechanism when the negative deviation charge is high. Finally, a sensitivity analysis reveals the effect of interval parameter setting upon optimization results. Moreover, a virtual power plant operator with a higher pessimism degree pursues higher profit with higher uncertainty.

Capable of aggregating multiple energy resources, the energy service company (ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitate the consumption of renewable resources in the energy market. However, the issues have become significantly more serious related to the privacy and security of the data in consumption and trading. In this paper, we address the problem by proposing a privacy-preserving energy scheduling (PPES) model based on energy blockchain network. A Lagrangian relaxation method is applied to decompose the model into several individual optimal scheduling problems, and the individual scheduling problems are solved by consensus algorithm and smart contracts in energy blockchain network. The performance of the proposed model and method is evaluated with several case studies based on multiple energy nodes. Simulation results show the rationality and validity of the proposed method, and the model is conducive to the protection of environment and transparent scheduling of energy service companies (ESCOs). In addition, it can reflect the information of energy demand and supply to improve the privacy and security of data.

This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of the distribution network. In particular, the reactive power capabilities of distributed generators and energy storage systems are fully utilized to minimize power losses and improve voltage profiles. The uncertainties pertaining to the forecasted values of renewable energy sources are modelled by scenario-based stochastic programming. The second-order cone programming relaxation method is used to deal with the nonlinear power flow constraints and transform the original mixed integer nonlinear programming problem into a tractable mixed integer second-order cone programming model, thus the difficulty of problem solving is significantly reduced. The 33-bus and 69-bus distribution networks are used to demonstrate the effectiveness of the proposed approach. Simulation results show that the proposed coordinated optimization approach helps improve the economic operation for active distribution network while improving the system security significantly.

Coordination of a hydropower, combined heat and power (CHP), and battery energy storage system (BESS) with multiple renewable energy sources (RES) can effectively reduce the adverse effects of large-scale renewable energy integration in power systems. This paper proposes a concept of a renewable-based hybrid energy system and puts forward an optimal scheduling model of this system, taking into account the cost of operation and risk. An optimization method is proposed based on Latin hypercube sampling, scene reduction, and piecewise linearization. Firstly, a large number of samples were generated with the Latin hypercube sampling method according to the uncertainties, including the renewable resources availability, the load demand, and the risk aversion coefficients, and the generated samples were reduced with a scene reduction method. Secondly, the piecewise linearization method was applied to convert nonlinear constraints into linear to obtain the best results of each scene. Finally, the performance of the proposed model and method was evaluated based on case studies with real-life data. Results showed that the renewable-based hybrid system can not only reduce the intermittent and volatility of renewable resources but also ensure the smooth of tie-line power as much as possible. The proposed model and method are universal, feasible, and effective.

Increasingly the prevalence of electric vehicles (EVs) calls for an effective planning of charging stations by coupling transportation network (TN) with power distribution network (PDN). This paper conducts an interdisciplinary study on the coordinated planning strategy of EV charging stations and coupled traffic-electric network. A comprehensive model is proposed, which determines the optimal expansion strategies for both TN and PDN, including sites and sizes of new charging stations, charging spots, TN lanes and PDN lines. In TN, we use an unconstrained traffic assignment model (UTAM) to explicitly capture the steady-state distribution of traffic flows. In PDN, operating conditions are described by a linearized DistFlow. Moreover, EV charging demands and interdependency of TN and PDN are characterized by EV charging station location model (ECSLM). A mixed-integer linear programming (MILP) with UTAM constraints is formulated for the coordinated planning model, where a global optimal solution is obtained through a deterministic branch-and-bound method. Numerical experiments on a coupled traffic-electric network are conducted to validate the effectiveness of the proposed model and the solution method.

Virtual power plant (VPP) aggregates distributed energy resources (DER) as a single profile to overcome scale disadvantage and dispersed characteristics. A commercial virtual power plant (CVPP) especially operates to improve the power‐market involvement. In this article, the CVPP medium‐term bidding strategy is optimized to obtain the profit interval mostly preferred by the CVPP's decision maker. Because the medium‐term forecast of renewable power generation and day ahead (DA) market price is far more difficult than short term, interval optimization is proposed, which does not require uncertain variable‐distribution assumption. The pessimism degree is introduced to quantify the decision maker's risk acceptance and compare intervals so as to solve the interval‐optimization model. Within the interval‐optimization framework, the bidding strategy between the bilateral contract (BC) and the DA market is figured out. A combined DA market deal and gas turbine generator strategy is proposed to cope with power shortage. Finally, an in‐depth sensitivity analysis is applied, and the characteristics and caution of interval optimization are deduced. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Line hardening refers to strengthening certain distribution lines that will not be subject to outages during extreme conditions. Line hardening provides a resilient solution in the case of major faults in a radial or meshed distribution system. We propose a robust optimal line hardening method coupled with multiple provisional microgrids to improve the distribution system resilience against worst N-k contingencies. A tri-level optimal model is considered with the objectives of minimizing the costs of line hardening and the operation of multiple islanded provisional microgrids, which could include the cost of load shedding in each provisional microgrid considering the worst N-k contingencies. Specifically, a two-stage robust optimization model is formulated and Benders decomposition (BD) algorithm coupled with iterative relaxation procedure (IRP) is developed to enable the tractable computation. In our work, the master problem determines line hardening strategies and the subproblem discovers the impact of the worst N-k contingencies corresponding to the optimal operation of reformed provisional microgrids. The second-stage max-min subproblem is solved efficiently using linearization techniques, duality theory and IRP. Our computational results for the IEEE distribution test systems validate the effectiveness of the proposed model and reveal that distributed generation is critical in increasing the resilience of distribution systems corresponding to the worst N-k contingencies in provisional microgrids.