Biblio

This paper puts forward a dynamic reduction method of renewable energy based on N-1 safety standard of power system, which is suitable for high-voltage distribution network and can reduce the abandoned amount of renewable energy to an ideal level. On the basis of AC sensitivity coefficient, the optimization method of distribution factor suitable for single line or multi-line disconnection is proposed. Finally, taking an actual high-voltage distribution network in Germany as an example, the simulation results show that the proposed method can effectively limit the line load, and can greatly reduce the line load with less RES reduction.

The total number of photovoltaic power producing facilities whose FIT-based ten-year contract expires by 2023 is expected to reach approximately 1.65 million in Japan. If the number of renewable electricity-producing/consuming facilities reached two million, an enormous number of transactions would be invoked beyond blockchain's scalability.We propose mutually cooperative two novel methods to simultaneously solve scalability, data size, and privacy problems in blockchain-based trading platforms for renewable energy environmental value. One is a management scheme of electricity production resources (EPRs) using an extended UTXO token. The other is a data aggregation scheme that aggregates a significant number of smart meter records with evidentiality using zero-knowledge proof (ZKP).

By 2025, it is estimated that installed data storage in the U.S. will be 2.2 Zettabytes, generating about 50 million units of end-of-life hard-disk drives (HDDs) per year. The circular economy (CE) tackles waste issues by maximizing value retention in the economy, for instance, through reuse and recycling. However, the reuse of hard disk drives is hindered by the lack of trust organizations have toward other means of data removal than physically destroying HDDs. Here, an agent-based approach explores how organizations' decisions to adopt other data removal means affect HDDs' circularity. The model applies the theory of planned behavior to model the decisions of HDDs end-users. Results demonstrate that the attitude (which is affected by trust) of end-users toward data-wiping technologies acts as a barrier to reuse. Moreover, social pressure can play a significant role as organizations that adopt CE behaviors can set an example for others.

Industry 4.0 or also known as the fourth industrial revolution poses a great cybersecurity risk for Supervisory control and data acquisition (SCADA) systems. Nowadays, lots of enterprises have turned into renewable energy and are changing the energy dependency to be on wind power. The SCADA systems are often vulnerable against different kinds of cyberattacks and thus allowing intruders to successfully and intrude exfiltrate different wind farm SCADA systems. During our research a future concept testbed of a wind farm SCADA system is going to be introduced. The already existing real-world vulnerabilities that are identified are later on going to be demonstrated against the test SCADA wind farm system.

In integrated gas and electricity energy systems, a catastrophic outage in one system could propagate to other, resulting in severe service interruption like what happened in 2021 Texas Blackout. To alleviate detrimental effects of these events, a coordinated effort must be adopted between integrated energy systems. In this paper, a central situational awareness system (CSAS) is developed to improve the coordination of operational resiliency measures by facilitating information sharing between power distribution systems (PDSs) and natural gas networks (NGNs) during emergency conditions. The CSAS collects operational data of the PDS and the NGN as well as data of upcoming weather condition, extracts the most vulnerable lines and pipelines, and accordingly obtains emergency actions. The emergency actions, i.e., optimal multi-microgrid formation, scheduling of distribution energy resources (DERs), and optimal electrical and gas load shedding plan, are optimized through a coupled graph-based approach with stochastic mixed integer linear programming (MILP) model. In the proposed model, uncertainties of renewable energy resources (RESs) is also considered. Numerical results on an integrated IEEE 33-bus and 30-node NGNs demonstrate the effectiveness of proposed CSAS.

Demand response (DR) is one of the most economical methods for peak demand reduction, renewable energy integration and ancillary service support. Residential electrical energy consumption takes approximately 33% of the total electricity usage and hence has great potentials in DR applications. However, residential DR encounters various challenges such as small individual magnitude, stochastic consuming patterns and privacy issues. In this study, we propose a stochastic optimal mechanism to tackle these issues and try to reveal the benefits from residential DR implementation. Stochastic residential load (SRL) models, a generation cost prediction (GCP) model and a stochastic optimal load aggregation (SOLA) model are developed. A set of uniformly distributed scalers is introduced into the SOLA model to efficiently avoid the peak demand rebound problem in DR applications. The SOLA model is further transformed into a deterministic LP model. Time-of-Use (TOU) tariff is adopted as the price structure because of its similarity and popularity. Case studies show that the proposed mechanism can significantly reduce the peak-to-average power ratio (PAPR) of the load profile as well as the electrical energy cost. Furthermore, the impacts of consumers' participation levels in the DR program are investigated. Simulation results show that the 50% participation level appears as the best case in terms system stability. With the participation level of 80%, consumers' electrical energy cost is minimized. The proposed mechanism can be used by a residential load aggregator (LA) or a utility to plan a DR program, predict its impacts, and aggregate residential loads to minimize the electrical energy cost.

Blockchain become a suitable platform for peer to peer energy trade as it facilitates secure interactions among parties with trust or a mutual trusted 3rd party. However, the scalability issue of blockchains is a problem for real-time energy trade to be completed within a small time duration. In this paper, we use offline channels for blockchains to circumvent scalability problems of blockchains for peer to peer energy trade with small trade duration. We develop algorithms to find stable coalitions for energy trade using blockchain offline channels. We prove that our solution is secure against adversarial prosumer behaviors, it supports real-time trade as the algorithm is guaranteed to find and record stable coalitions before a fixed time, and the coalition structure generated by the algorithm is efficient.

Local flexibility markets are a concept to integrate distributed flexibilities such as power-to-gas, power-to-heat systems, electric vehicles, and battery storage systems into congestion management of distribution and transmission grids. However, the activation of the flexibility of storage systems changes their state-of-charge and thus reduces their available flexibility. Counter-trading or compensation of activated flexibility in later points of time lets storage operators regain flexibility. However, this compensation might create or amplify congestions when not supervised by system operators. Therefore, we propose the inclusion of compensation within the flexibility market clearing process by system operators. We further develop a linear formulation of flexibility market bids by storage systems that takes the need for compensation into account. For this, first, the operational planning formulation of a storage system is expanded by flexibility market participation. Subsequently, the linear formulation of bids is derived and demonstrated.

The progressive expansion of renewable energies, especially wind power plants being promoted in Germany as part of the energy transition, places new demands on the transmission grid. As an alternative to grid expansion, sector coupling of the gas and electricity sector through Power-to-Gas (PtG) technology is seen as a great opportunity to make the energy transmission more flexible and reliable in the future as well as make use of already existing gas infrastructure. In this paper, PtG plants are dimensioned and placed in a model of the German transmission grid. Time series based load flow calculations are performed allowing conclusions about the line loading for the exemplary year 2016.

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 advancement and penetration of distributed energy resources (DERs) and renewable energy sources (RES) are transforming legacy energy systems in an attempt to reduce carbon emissions and energy waste. Demand Response (DR) has been identified as a key enabler of integrating these, and other, Smart Grid technologies, while, simultaneously, ensuring grid stability and secure energy supply. The massive deployment of smart meters, IoT devices and DERs dictate the need to move to decentralized, or even localized, DR schemes in the face of the increased scale and complexity of monitoring and coordinating the actors and devices in modern smart grids. Furthermore, there is an inherent need to guarantee interoperability, due to the vast number of, e.g., hardware and software stakeholders, and, more importantly, promote trust and incentivize the participation of customers in DR schemes, if they are to be successfully deployed.In this work, we illustrate the design of an energy system that addresses all of the roadblocks that hinder the large scale deployment of DR services. Our DR framework incorporates modern Smart Grid technologies, such as fog-enabled and IoT devices, DERs and RES to, among others, automate asset handling and various time-consuming workflows. To guarantee interoperability, our system employs OpenADR, which standardizes the communication of DR signals among energy stakeholders. Our approach acknowledges the need for decentralization and employs blockchains and smart contracts to deliver a secure, privacy-preserving, tamper-resistant, auditable and reliable DR framework. Blockchains provide the infrastructure to design innovative DR schemes and incentivize active consumer participation as their aforementioned properties promote transparency and trust. In addition, we harness the power of smart contracts which allows us to design and implement fully automated contractual agreements both among involved stakeholders, as well as on a machine-to-machine basis. Smart contracts are digital agents that "live" in the blockchain and can encode, execute and enforce arbitrary agreements. To illustrate the potential and effectiveness of our smart contract-based DR framework, we present a case study that describes the exchange of DR signals and the autonomous instantiation of smart contracts among involved participants to mediate and monitor transactions, enforce contractual clauses, regulate energy supply and handle payments/penalties.

In recent years the use of security gateways (SG) located within the electrical grid distribution network has become pervasive. SGs in substations and renewable distributed energy resource aggregators (DERAs) protect power distribution control devices from cyber and cyber-physical attacks. When encrypted communications within a DER network is used, TCP/IP packet inspection is restricted to packet header behavioral analysis which in most cases only allows the SG to perform anomaly detection of blocks of time-series data (event windows). Packet header anomaly detection calculates the probability of the presence of a threat within an event window, but fails in such cases where the unreadable encrypted payload contains the attack content. The SG system log (syslog) is a time-series record of behavioral patterns of network users and processes accessing and transferring data through the SG network interfaces. Threatening behavioral pattern in the syslog are measurable using both anomaly detection and graph theory. In this paper it will be shown that it is possible to efficiently detect the presence of and classify a potential threat within an SG syslog using light-weight anomaly detection and graph theory.

The massive integration of Renewable Energy Sources (RES) into power systems is a major challenge but it also provides new opportunities for network operation. For example, with a large amount of RES available at HV subtransmission level, it is possible to exploit them as controlling resources in islanding conditions. Thus, a procedure for off-line evaluation of islanded operation feasibility in the presence of RES is proposed. The method finds which generators and loads remain connected after islanding to balance the island's real power maximizing the amount of supplied load and assuring the network's long-term security. For each possible islanding event, the set of optimal control actions (load/generation shedding) to apply in case of actual islanding, is found. The procedure is formulated as a Mixed Integer Non-Linear Problem (MINLP) and is solved using Genetic Algorithms (GAs). Results, including dynamic simulations, are shown for a representative HV subtransmission grid.

In order to be more environmentally friendly, a lot of parts and aspects of life become electrified to reduce the usage of fossil fuels. This can be seen in the increased number of electrical vehicles in everyday life. This of course only makes a positive impact on the environment, if the electricity is produced environmentally friendly and comes from renewable sources. But when the green electrical power is produced, it still needs to be transported to where it's needed, which is not necessarily near the production site. In China, one of the ways to do this transport is to use High Voltage Direct Current (HVDC) technology. This of course means, that the current has to be converted to DC before being transported to the end user. That implies that the converter stations are of great importance for the grid security. Therefore, a precise monitoring of the stations is necessary. Ideally, this could be accomplished with wireless sensor nodes with an autarkic energy supply. A role in this energy supply could be played by a thermoelectrical generator (TEG). But to assess the power generated in the specific environment, a simulation would be highly desirable, to evaluate the power gained from the temperature difference in the converter station. This paper proposes a method to simulate the generated power by combining a model for the generator with a Computational Fluid Dynamics (CFD) model converter.

The aim of this paper is to explore the performance of two well-known wave energy converters (WECs) namely Floating Buoy Point Absorber (FBPA) and Oscillating Surge (OS) in onshore and offshore locations. To achieve clean energy targets by reducing greenhouse gas emissions, integration of renewable energy resources is continuously increasing all around the world. In addition to widespread renewable energy source such as wind and solar photovoltaic (PV), wave energy extracted from ocean is becoming more tangible day by day. In the literature, a number of WEC devices are reported. However, further investigations are still needed to better understand the behaviors of FBPA WEC and OS WEC under irregular wave conditions in onshore and offshore locations. Note that being surrounded by Bay of Bengal, Bangladesh has huge scope of utilizing wave power. To this end, FBPA WEC and OS WEC are simulated using the typical onshore and offshore wave height and wave period of the coastal area of Bangladesh. Afterwards, performances of the aforementioned two WECs are compared by analyzing their power output.

Mutriku wave farm is the first commercial plant all around the world. Since July 2011 it has been continuously selling electricity to the grid. It operates with the OWC technology and has 14 operating Wells-type turbines. In the plant there is a SCADA data recording system that collects the most important parameters of the turbines; among them, the pressure in the inlet chamber, the position of the security valve (from fully open to fully closed) and the generated power in the last 5 minutes. There is also an electricity meter which provides information about the amount of electric energy sold to the grid. The 2014 winter (January, February and March), and especially the first fortnight of February, was a stormy winter with rough sea state conditions. This was reflected both in the performance of the turbines (high pressure values, up to 9234.2 Pa; low opening degrees of the security valve, down to 49.4°; and high power generation of about 7681.6 W, all these data being average values) and in the calculated capacity factor (CF = 0.265 in winter and CF = 0.294 in February 2014). This capacity factor is a good tool for the comparison of different WEC technologies or different locations and shows an important seasonal behavior.

This article presents a consensus based distributed energy management optimization algorithm for an islanded microgrid. With the rapid development of renewable energy and distributed generation (DG) energy management is becoming more and more distributed. To solve this problem a multi-agent system based distributed solution is designed in this work which uses lambda-iteration method to solve optimization problem. Moreover, the algorithm is fully distributed and transmission losses are also considered in the modeling process which enhanced the practicality of proposed work. Simulations are performed for different cases on 8-bus microgrid to show the effectiveness of algorithm. Moreover, a scalability test is performed at the end to further justify the expandability performance of algorithm for more advanced networks.

The advent of smart grids offers us the opportunity to better manage the electricity grids. One of the most interesting challenges in the modern grids is the consumer demand management. Indeed, the development in Information and Communication Technologies (ICTs) encourages the development of demand-side management systems. In this paper, we propose a distributed energy demand scheduling approach that uses minimal interactions between consumers to optimize the energy demand. We formulate the consumption scheduling as a constrained optimization problem and use game theory to solve this problem. On one hand, the proposed approach aims to reduce the total energy cost of a building's consumers. This imposes the cooperation between all the consumers to achieve the collective goal. On the other hand, the privacy of each user must be protected, which means that our distributed approach must operate with a minimal information exchange. The performance evaluation shows that the proposed approach reduces the total energy cost, each consumer's individual cost, as well as the peak to average ratio.

Cyber security is a crucial factor for modern power system as many applications are heavily relied on the result of state estimation. Therefore, it is necessary to assess and enhance cyber security for new applications in power system. As an emerging technology, smart grid topology control has been investigated in stability and reliability perspectives while the associated cyber security issue is not studied before. In successful false data injection attack (FDIA) against AC state estimation, attacker could alter online stability check result by decreasing real power flow measurement on the switching target line to undermine physical system stability in topology control. The physical impact of FDIA on system control operation and stability are illustrated. The vulnerability is discussed on perfect FDIA and imperfect FDIA against residue based bad data detection and corresponding countermeasure is proposed to secure critical substations in the system. The vulnerability and countermeasure are demonstrated on IEEE 24 bus reliability test system (RTS).

{This paper describes application of permanent magnet on permanent magnet generator (PMG) for renewable energy power plants. Permanent magnet used are bonded hybrid magnet that was a mixture of barium ferrite magnetic powders 50 wt % and NdFeB magnetic powders 50 wt % with 15 wt % of adhesive polymer as a binder. Preparation of bonded hybrid magnets by hot press method at a pressure of 2 tons and temperature of 200°C for 15 minutes. The magnetic properties obtained were remanence induction (Br) =1.54 kG, coercivity (Hc) = 1.290 kOe, product energy maximum (BHmax) = 0.28 MGOe, surface remanence induction (Br) = 1200 gauss

In this paper, we focus on energy management of distributed generators (DGs) and energy storage system (ESS) in microgrids (MG) considering uncertainties in renewable energy and load demand. The MG energy management problem is formulated as a two-stage stochastic programming model based on optimization principle. Then, the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables. A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load demand. Finally, the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization toolbox. The simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.