Day-ahead electricity price forecasting via the application of artificial neural network based models

There is an increasing interest and research effort focused on the analysis, design and implementation of distributed control systems for AC , DC and hybrid AC / DC microgrids. It is claimed that distributed controllers have several advantages over centralised control schemes, e.g., improved reliability, flexibility, controllability, black start operation, robustness to failure in the communication links, etc. In this work, an overview of the state-of-the-art of distributed cooperative control systems for isolated microgrids is presented. Protocols for cooperative control such as linear consensus, heterogeneous consensus and finite-time consensus are discussed and reviewed in this paper. Distributed cooperative algorithms for primary and secondary control systems, including (among others issues) virtual impedance, synthetic inertia, droop-free control, stability analysis, imbalance sharing, total harmonic distortion regulation, are also reviewed and discussed in this survey. Tertiary control systems, e.g., for economic dispatch of electric energy, based on cooperative control approaches, are also addressed in this work. This review also highlights existing issues, research challenges and future trends in distributed cooperative control of microgrids and their future applications.

/pdf/distributed-control-strategies-for-microgrids-an-overview-b0opeslrl1.pdf

Distributed Control Strategies for Microgrids: An Overview

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

The energy consumption by non-residential consumers in China accounts for a significant proportion of the total energy consumption in the society. Thus, accurate non-residential load forecasting plays an increasingly essential role in the future grid planning and operation. In this paper, a method based on LSTM recurrent neural network is proposed to predict the load of non-residential consumers using multiple correlated sequence information. First, the k-means is employed to analyze the daily load curves of non-residential consumers, classify and mine the consumer’s energy consumption behavior patterns. Then, the Spearman correlation coefficient is applied to investigate the time correlation under multiple time series for non-residential consumers. It is found that there exist multiple related time sequences such as adjacent time points, the same time points in adjacent days, and the same day in adjacent weeks among data samples for a specific consumer. Therefore, a non-residential load forecasting framework based on multiple sequence LSTM recurrent neural networks is presented. The proposed framework is tested on a real data set, which contains 48 non-residential consumers’ energy consumption data in China, and outperforms the other load forecasting approaches. Experiment results show that this method can effectively utilize the multiple sequence information, and successfully capture the dependencies among these sequences.

Short-Term Non-Residential Load Forecasting Based on Multiple Sequences LSTM Recurrent Neural Network

The increased rate of cyber-attacks on the power system necessitates the need for innovative solutions to ensure its resiliency. This work builds on the advancement in the IoT to provide a practical framework that is able to respond to multiple attacks on a network of interconnected microgrids. This paper provides an IoT-based digital twin (DT) of the cyber-physical system that interacts with the control system to ensure its proper operation. The IoT cloud provision of the energy cyber-physical and the DT are mathematically formulated. Unlike other cybersecurity frameworks in the literature, the proposed one can mitigate an individual as well as coordinated attacks. The framework is tested on a distributed control system and the security measures are implemented using cloud computing. The physical controllers are implemented using single-board computers. The practical results show that the proposed DT is able to mitigate the coordinated false data injection and the denial of service cyber-attacks.

On the Implementation of IoT-Based Digital Twin for Networked Microgrids Resiliency Against Cyber Attacks

This paper presents a hierarchical control framework for implementing primary frequency regulation (PFR) by using residential heating, ventilating, and air-conditioning (HVAC) loads. The proposed control system consists of a load aggregator, a central controller, and a group of local controllers. The load aggregator is responsible for managing a population of HVAC loads as a virtual generator to participate in primary reserve market. The central controller dispatches the HVAC loads by assigning a triggering frequency to each HVAC load. A temperature-priority-list method is applied to avoid frequent switching on/off HVAC units when implementing PFR. The local controllers determine the ON/OFF status of HVAC units autonomously based on their triggering frequencies. A self-adjustment algorithm is proposed to correct the error in the assigned triggering frequency by considering minimum ON/OFF time of HVAC units. The proposed control framework can significantly reduce the communication needs. The simulation results indicate that by adopting the proposed control framework, one can dispatch HVAC units more efficiently at the central control level while achieve fast response at the local control level.

Hierarchical Control of Residential HVAC Units for Primary Frequency Regulation

This paper presents a two-layer distributed cooperative control method for networked microgrid (NMG) systems, taking into account the proprietary nature of microgrid (MG) owners. The proposed control architecture consists of an MG-control layer and an NMG-control layer. In the MG layer, the primary and distributed secondary controls realize accurate power sharing among distributed generators (DGs) and the frequency/voltage reference following within each MG. In the NMG layer, the tertiary control enables regulation of the power flowing through the point of common coupling (PCC) of each MG in a decentralized manner. Furthermore, distributed quaternary control restores the system frequency and critical bus voltage to their nominal values and ensures accurate power sharing among MGs. A small-signal dynamic model is developed to evaluate the dynamic performance of NMG systems with the proposed control method. Time-domain simulations and experiments on NMG test systems are performed to validate the effectiveness of the proposed method.

/pdf/a-two-layer-distributed-cooperative-control-method-for-1qah8rmzv1.pdf

A Two-Layer Distributed Cooperative Control Method for Islanded Networked Microgrid Systems

The two-layer distributed control architecture, including the microgrid (MG) control layer and MG cluster (MGC) control layer, can be used for interconnecting multiple MGs to form the MGC. However, the coupling among multiple elements and the interaction between two control layers may introduce new low-damping oscillatory modes and even reduce the stability margin. Unfortunately, the detailed small-signal stability analysis and stability enhancement method of the MGC with the two-layer distributed control strategy have not been reported. To fill this gap, this paper first presents a unified small-signal dynamic model of the MGC. Subsequently, a comprehensive small-signal stability analysis based on the model is presented to analyze: 1) the mechanism of coupling/interaction among MGs and multiple control layers; 2) the participation factors of the low-damping modes; and 3) the sensitivity of the distributed control parameters. Moreover, the design of the distributed control parameters is formulated as an optimization problem, where the particle swarm optimization is employed to search for an optimal combination of parameters to enhance system stability. Finally, the stability assessment and time-domain simulation are carried out to validate the effectiveness of the proposed method.

/pdf/small-signal-stability-analysis-and-optimal-parameters-2t43fbu7jc.pdf

Small-Signal Stability Analysis and Optimal Parameters Design of Microgrid Clusters

A day-ahead market clearing price forecasting method based on the Takagi-Sugeno model and the adaptive neuro-fuzzy inference system (ANFIS) is proposed. First, the structure of ANFIS is determined by subtractive clustering; then the premise parameters and consequent parameters of ANFIS are identified by the hybrid learning algorithm; finally, related factors that influence future daily electricity prices are input into the ANFIS to forecast next-day electricity prices. By use of the data of California Electricity Market in 1999, the forecasting model is constructed and the anticipated electricity prices of the next day are implemented. The forecasting results show that the forecasting model established by us is valid.

An ANFIS model of electricity price forecasting based on subtractive clustering

Machine learning methods are the main stream algorithms applied in short term load forecasting. However, typical machine learning methods consisting of Artificial Neural Network (ANN) and Support Vector Regression (SVR) have deficiencies hard to overcome, such as easy to be trapped in local optimization (for ANN) or hard to decide kernel parameter and penalty parameter (for SVR). On the other hand, grey relational analysis is an effective method to select proper historical data as training set for training machine learning models. But it is not comprehensive and accurate enough. In this paper, a new two-stage hybrid algorithm aimed to solve these two problems is proposed. Random Forest (RF) method is introduced as the machine learning method, which will not cause overfitting problem and parameters are easy to be tuned. Furthermore, Grey Relational Projection (GRP) is introduced to select similar historical data to train random forest models. The final forecasting results based on real load data prove this new two-stage method performs better than the other two common methods.

Xiaoyu Wu

Papers

Hierarchical Control of Residential HVAC Units for Primary Frequency Regulation

A Two-Layer Distributed Cooperative Control Method for Islanded Networked Microgrid Systems

Small-Signal Stability Analysis and Optimal Parameters Design of Microgrid Clusters

An ANFIS model of electricity price forecasting based on subtractive clustering

A two-stage random forest method for short-term load forecasting