Biblio

Various approaches of microgrid operation have been proposed, albeit with noticeable issues such as power-sharing, control of frequency and voltage excursions, applicability on different grids, etc. This paper proposes a goal function-based, decentralized control that addresses the mentioned problems and secures the microgrid stability by constraining the frequency and node deviations across the grid while simultaneously supporting the desired active power exchange between prosumer nodes. The control algorithm is independent of network topology and enables arbitrary node connection, i.e. seamless microgrid expandability. To confirm the effectiveness of the proposed control strategy, simulation results are presented and discussed.

There is momentous attention from researchers and practitioners all over the world towards one of the most advanced trends in the world, Smart cities. A smart city is an efficient and sustainable city that offers a superior life quality to all human beings through the optimum management of all its resources. Optimum energy management technique within the smart city is a challenging environment that needs a full focus on basic important needs and supports of the smart city. This includes Smart Grid (SG) infrastructure, Distributed Generation (DG) technology, Smart Home Energy Management System (HEMS), Smart Transportation System (STS), and Energy Storage System (ESS). Out of these five taxonomies, there have been some disputes addressed in profitability and security due to the major involvement of electromobility in the smart transportation system. It creates a big impact on the smart city environment. The disputes in profitability can be effectively handled with the use of dynamic pricing techniques and peer-to-peer (P2P) energy trading mechanisms. On the other hand, security disputes can be overwhelmed by the use of blockchain technology. This paper reviews the energy management-related work on smart cities with the consideration of these basic important needs and supports.

Electrical load forecasting is an essential part of the smart grid to maintain a stable and reliable grid along with helping decisions for economic planning. With the integration of more renewable energy resources, especially solar photovoltaic (PV), and transitioning into a prosumer-based grid, electrical load forecasting is deemed to play a crucial role on both regional and household levels. However, most of the existing forecasting methods can be considered black-box models due to deep digitalization enablers, such as Deep Neural Networks (DNN), where human interpretation remains limited. Additionally, the black box character of many models limits insights and applicability. In order to mitigate this shortcoming, eXplainable Artificial Intelligence (XAI) is introduced as a measure to get transparency into the model’s behavior and human interpretation. By utilizing XAI, experienced power market and system professionals can be integrated into developing the data-driven approach, even without knowing the data science domain. In this study, an electrical load forecasting model utilizing an XAI tool for a Norwegian residential building was developed and presented.

To solve the problem of poor security and performance caused by traditional encryption algorithm in the cloud data storage of power bidding system, we proposes a hybrid encryption method based on symmetric encryption and asymmetric encryption. In this method, firstly, the plaintext upload file is divided into several blocks according to the proportion, then the large file block is encrypted by symmetrical encryption algorithm AES to ensure the encryption performance, and then the small file block and AES key are encrypted by asymmetric encryption algorithm ECC to ensure the file encryption strength and the security of key transmission. Finally, the ciphertext file is generated and stored in the cloud storage environment to prevent sensitive files Pieces from being stolen and destroyed. The experimental results show that the hybrid encryption method can improve the anti-attack ability of cloud storage files, ensure the security of file storage, and have high efficiency of file upload and download.

In this paper, according to the electricity business process including collecting and transmitting power information and sending control instructions, a coupling model of control-communication flow is built which is composed of three main matrices: control-communication, communication-communication, communication-control incidence matrices. Furthermore, the effective path change between two communication nodes is analyzed and a calculation method of connectivity probability for information network is proposed when considering a breakdown in communication links. Then, based on Bayesian conditional probability theory, the effect of the communication interruption on the energy Internet is analyzed and the metric matrix of controllability is given under communication congestion. Several cases are given in the final of paper to verify the effectiveness of the proposed method for calculating controllability matrix by considering different link interruption scenarios. This probability index can be regarded as a quantitative measure of the controllability of the power service based on the communication transmission instructions, which can be used in the power business decision-making in order to improve the control reliability of the energy Internet.

Nowadays, with the diversification and decentralization of energy systems, the energy Internet makes it possible to interconnect distributed energy sources and consumers. In the energy trading market, the traditional centralized model relies entirely on trusted third parties. However, as the number of entities involved in the transactions grows and the forms of transactions diversify, the centralized model gradually exposes problems such as insufficient scalability, High energy consumption, and low processing efficiency. To address these challenges, we propose a secure and efficient energy renewable trading scheme based on blockchain. In our scheme, the electricity market trading model is divided into two levels, which can not only protect the privacy, but also achieve a green computing. In addition, in order to adapt to the relatively weak computing power of the underlying equipment in smart grid, we design a credibility-based equity proof mechanism to greatly improve the system availability. Compared with other similar distributed energy trading schemes, we prove the advantages of our scheme in terms of high operational efficiency and low computational overhead through experimental evaluations. Additionally, we conduct a detailed security analysis to demonstrate that our solution meets the security requirements.

The need to enhance the performance of existing transmission network in line with economic and technical constraints is crucial in a competitive market environment. This paper models the total transfer capacity (TTC) improvement using optimally placed thyristor-controlled series capacitors (TCSC). The system states were evaluated using distributed slack bus (DSB) and continuous power flow (CPF) techniques. Adaptable logic relations was modelled based on security margin (SM), steady state and transient condition collapse voltages (Uss, Uts) and the steady state line power loss (Plss), through which line suitability index (LSI) were obtained. The fuzzy expert system (FES) membership functions (MF) with respective degrees of memberships are defined to obtain the best states. The LSI MF is defined high between 0.2-0.8 to provide enough protection under transient disturbances. The test results on IEEE 30 bus system show that the model is feasible for TTC enhancement under steady state and N-1 conditions.

This paper proposes a lightweight and privacy-preserving data aggregation scheme for dynamic electricity pricing based billing in smart grids using the concept of single-pass authenticated encryption (AE). Unlike existing literature that only considers static pricing, to the best of our knowledge, this is the first paper to address privacy under dynamic pricing.

A significant number of the generation, transmission and distribution facilities in the North America were designed and configured for serving electric loads and economic activities under certain reliability and resiliency requirements over 30 years ago. With the changing generation mix, the electric grid is tasked to deliver electricity made by fuel uncertain and energy limited resources. How adequate are the existing facilities to meet the industry expectations on reliability? What level of grid resiliency should be designed and built to sustain reliable electric services given the increasing exposure to frequent and lasting severe weather conditions? There is a need to review the modeling assumptions, operating and maintenance records before we can answer these questions.

This study proposes to apply an efficient formulation to solve the stochastic security-constrained generation capacity expansion planning (GCEP) problem using an improved method to directly compute the generalized generation distribution factors (GGDF) and the line outage distribution factors (LODF) in order to model the pre- and the post-contingency constraints based on the only application of the partial transmission distribution factors (PTDF). The classical DC-based formulation has been reformulated in order to include the security criteria solving both pre- and post-contingency constraints simultaneously. The methodology also takes into account the load uncertainty in the optimization problem using a two-stage multi-period model, and a clustering technique is used as well to reduce load scenarios (stochastic problem). The main advantage of this methodology is the feasibility to quickly compute the LODF especially with multiple-line outages (N-m). This idea could speed up contingency analyses and improve significantly the security-constrained analyses applied to GCEP problems. It is worth to mentioning that this approach is carried out without sacrificing optimality.

This paper is concerned about the security vulnerabilities in the implementation of the Congestion Revenue Rights (CRR) markets. Such problems may be due to the weakly detectable network model parameter errors which are commonly found in power systems. CRRs are financial tools for hedging the risk of congestion charges in power markets. The reimbursements received by CRR holders are determined by the congestion patterns and Locational Marginal Prices (LMPs) in the day-ahead markets, which heavily rely on the parameters in the network model. It is recently shown that detection of errors in certain network model parameters may be very difficult. This paper's primary goal is to illustrate the lack of market security due to such vulnerabilities, i.e. CRR market calculations can be manipulated by injecting parameter errors which are not likely to be detected. A case study using the IEEE 14-bus system will illustrate the feasibility of such undetectable manipulations. Several suggestions for preventing such cyber security issues are provided at the end of the paper.