Distribution System Modeling and Analysis

This paper proposes a data-driven distributionally robust co-optimization model for the peer-to-peer (P2P) energy trading and network operation of interconnected microgrids (MGs). In particular, three-phase unbalanced MG networks are considered to account for the implementation practices, and the emerging soft open point (SOP) technology is used for the flexible connection of the multi-MGs. First, the energy management in individual MGs is modeled as a distributionally robust optimization (DRO) problem considering the P2P energy trading options and various operational constraints. Later, a novel decentralized and incentive-compatible pricing strategy is developed for P2P energy trading using the alternating direction method of multipliers (ADMM). Furthermore, the uncertainties in load consumption and renewable generation (RG) are taken into account and the Wasserstein metric (WM) is used to construct the ambiguity set of the uncertainty probability distributions (PDs). Consequently, only historical data is needed rather than prior knowledge about the actual PDs. Finally, the equivalent linear programming reformulations are derived for the DRO model to achieve computational tractability. Numerical tests on four interconnected MGs corroborate the advantages of the proposed P2P energy trading scheme and also demonstrate that the proposed DRO model is more effective in handling the uncertainties compared to the robust optimization (RO) and the stochastic programming (SP) models.

Data-Driven Distributionally Robust Co-Optimization of P2P Energy Trading and Network Operation for Interconnected Microgrids

An accurate and reliable traffic flow prediction is of great significance, especially the long-term traffic flow prediction e.g., 24 hours, which can help the traffic decision-makers formulate the future traffic management strategy. However, the long-term traffic flow prediction imposes great challenges for decision-makers due to the nonlinear and chaotic feature of traffic flow. Therefore, in this paper, we proposed a hybrid deep learning model based on wavelet decomposition, convolutional neural network-long and short-term memory neural network (CNN-LSTM), called W-CNN-LSTM, to prediction next-day traffic flow. The wavelet decomposition technology is used to decompose the original traffic flow data into high-frequency data and low-frequency data for the improvement of predictive accuracy. The decomposed sequences are fed into a CNN-LSTM deep learning model, where the long-term temporal features of traffic flow can be well captured and learned. The numerical experiment is carried out against five benchmarks based on England traffic flow dataset; the results show that the proposed hybrid approach can achieve superior forecasting skill over the benchmarks.

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A Hybrid Deep Learning Framework for Long-Term Traffic Flow Prediction

The large-scale integration of renewable energy sources in the power system can render instability issues, which degrades the power quality of the voltage supply. To address this issue, the electric springs (ES), which are elastic load demand control technologies, have been reported. Since it was proposed in 2011, many literatures on the applications, circuit topologies, control designs and the stability analysis of the ES have been reported. Based on the reported literatures, a comprehensive review of AC and DC ES technologies has been performed in this paper. The research outcomes are systematically summarized. The trend of the potential development of ES technologies is envisioned.

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A Review of AC and DC Electric Springs

This article proposes a new control strategy for the voltage regulation of unbalanced low-voltage (LV) distribution grids with high penetration of distributed renewable energy sources (DRESs). The proposed method uses the available reactive power of DRESs as the primary means for voltage regulation. Furthermore, its distinct feature is the use of a distributed control architecture prioritizing the response of DRESs to maintain the network voltages within permissible limits and minimize the network losses. The prioritization process is locally implemented by each DRES combining two types of information: (a) the sensitivity matrix that quantifies the impact of reactive power variations on the network voltages and (b) voltage measurements along the network. Time-domain and time-series simulations on the IEEE European LV test feeder are performed to evaluate the performance of the proposed method against existing decentralized, distributed and centralized, optimization-based methods.

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Distributed Reactive Power Control Scheme for the Voltage Regulation of Unbalanced LV Grids

The unbalanced nature of the distribution networks (DNs) and communication asynchrony pose considerable challenges to the distributed voltage regulation. In this paper, two distributed voltage control algorithms are proposed to overcome these challenges in multiphase unbalanced DNs. The proposed algorithms can be leveraged in online implementations to cope with the fast-varying system operating conditions. By adopting the linearized multiphase DistFlow model, the voltage control problem is formulated as a convex quadratic programming problem for which a synchronous distributed algorithm is developed based on the dual ascent method. To account for communication delays, an asynchronous distributed algorithm is proposed evolving from the synchronous one by incorporating an event-triggered communication protocol. Furthermore, closed-form solutions to the optimization subproblems are derived to enhance the computational efficiency, and communication complexity is reduced significantly to the extent that only neighborhood information exchange is required. Finally, the convergence of the proposed algorithms to the global optimality is established analytically. Numerical tests on the IEEE 123-bus network not only corroborate that our proposed algorithms are more efficient in eliminating voltage violations and minimizing network loss compared with two benchmarks but also validate their effectiveness for online implementations.

Distributed Online VAR Control for Unbalanced Distribution Networks With Photovoltaic Generation

The invention discloses a wind-solar-biogas multi-microgrid system and an equivalent energy and communication transaction method thereof. The equivalent energy and communication transaction method ofthe wind-solar-biogas multi-microgrid system comprises the following steps: building a microgrid optimization scheduling model; establishing a multi-microgrid system resource transaction problem mathematical model according to the microgrid optimization scheduling model and a Nash bargaining theory; decomposing a resource transaction problem into sub-problems decided by each micro-grid, and solving the sub-problems by each micro-grid to solve the multi-micro-grid system resource transaction problem mathematical model; and after solving the multi-micro-grid system resource transaction problem mathematical model, obtaining an optimal energy and communication scheduling strategy, and distributing the income of each microgrid to realize optimal cooperative operation of the multi-microgrid system. According to the technical scheme, the communication traffic of a plurality of interconnected micro-grids in the wind-solar-biogas multi-micro-grid system is reduced, and information privacy is protected.

Liu Chengying

Papers

Distributed Online VAR Control for Unbalanced Distribution Networks With Photovoltaic Generation

Wind-solar-biogas multi-microgrid system and equivalent energy and communication transaction method thereof