Voltage stability assessment is essential for maintaining reliable power grid operations. Stability assessment approaches using deep learning address the shortfalls of the traditional time-domain simulation-based approaches caused by increased system complexity. However, deep learning models are shown to be vulnerable to adversarial examples in the field of computer vision. While this vulnerability has been noticed by the power grid cybersecurity research, the domain-specific analysis on the requirements imposed upon effective attack implementation is still lacking. Although these attack requirements are usually reasonable in computer vision tasks, they can be stringent in the context of power grids. In this paper, we conduct a systematic investigation on the attack requirements and credibility of six representative adversarial example attacks based on a voltage stability assessment application for the New England 10-machine 39-bus power system. We show that (1) compromising about half the transmission system buses’ voltage traces is a rule-of-thumb attack requirement; (2) the universal adversarial perturbations regardless of the original clean voltage trajectory possess the same credibility as the widely studied false data injection attacks on power grid state estimation, while the input-specific adversarial perturbations are less credible; (3) the prevailing strong adversarial training thwarts the universal perturbations but fails in defending certain input-specific perturbations. To advance defense to cope with both universal and input-specific adversarial examples, we propose a new approach that simultaneously estimates the predictive uncertainty of any given input of voltage trajectory and thwarts the attacks effectively.

The economic operation of lithium-ion battery energy storage in electricity markets requires optimally balancing the tradeoff between maximizing the revenue from energy arbitrage and minimizing the capacity loss due to usage. This optimal balance can be achieved by incorporating the stress due to the depth of discharge and battery temperatures in the optimal dispatch framework. However, the stress models are nonlinear and the quantification of partial charge–discharge cycles requires the rainflow cycle counting algorithm, which does not have an analytical form. Considering the challenges, a set of physics-inspired sufficient conditions are developed to handle the nonanalytical form of therainflowalgorithm and to consider cell-level temperatures. The proposed stress cognizant optimal battery dispatch (SC-OBD) framework is applied to a battery participating in both the day-ahead and real-time balancing market. A model predictive control-based framework is proposed to handle uncertain electricity prices in the real-time market and to guarantee the fulfilment of day-ahead market commitments. The numerical results indicate that the proposed SC-OBD can efficiently utilize the cooling to reduce degradation with/without modifying the market-benchmark dispatch.

The distribution of measurement noise applied in practical power system state estimation (PSSE) can deviate from the assumed Gaussian model, and the performance of an estimator becomes bad if the Gaussian model is still used. This article proposes a new distributed robust PSSE method for multiarea power systems. The non-Gaussian model is utilized to fit the measurement noise distribution to reach high model accuracy. The proposed distributed method is derived based on the maximum correntropy criterion to further reduce the impact of non-Gaussian measurement noise and outliers. The influence function combined with the finite-time average consensus algorithm is used to implement the proposed distributed robust method in a fully distributed manner. Simulations conducted on the IEEE 30-bus, 118-bus and 300-bus systems and the Polish 2383-bus system demonstrate the robustness and effectiveness of the proposed distributed method. Each local area can get the system-wide robust state estimation solution by only using local information and small amounts of data from neighboring areas. Our proposed distributed robust method has at least 12% improvement in reducing the mean squared error under Gaussian-Uniform noise. It is verified the communication network applied for the proposed method is fairly flexible while the existing distributed approaches use a fixed communication network when the power systems are partitioned. The simulation results also verify the robustness of the proposed method to bad data and communication failure.

The optimal frequency control of autonomous microgrids (MGs), i.e., to achieve fast frequency recovery and dynamic power adjustment of the distributed generators in proportion to predefined participation factors, can be achieved in a fully distributed way based on the subgradient consensus protocol. However, such a distributively controlled MG is susceptible to different types of cyber attacks infiltrated from different locations. In this article, a robust and resilient distributed optimal frequency control scheme is proposed to address the threat of cyber attacks. It is facilitated by introducing an auxiliary networked system interconnecting with the original cooperative control system. On condition that the cyber attacks are within certain ranges, the robust design can maintain the functionalities by significantly attenuating the impact. Otherwise, the cyber attacks can be easily detected, and resilient reactions can be taken to mitigate their influences via isolation. Simulation results in a modified IEEE 34-bus MG validate the effectiveness of the proposed approach.

Control systems are significant to the microgrid as they regulate performance parameters such as frequency, active power, and voltage. Distributed control systems allow direct communication between the secondary controllers and controls the parameters efficiently. To secure each distributed control process and ensure a good quality of control results, a proof-of-authority private blockchain is applied in this article to defend the distributed control system against various types of cyber-attacks such as false data injection. A four-distributed generation islanded microgrid is tested with the implementation of the blockchain. Smart contracts are created to calculate the control feedback and return the value to corresponding secondary controllers. All of the four nodes are initially assigned as the authority nodes to share the mining burden, but according to the proof-of-authority consensus protocol, the authority role could be excluded if the node behaves illegally and causes damage to the control system. In addition, different attacking scenarios are categorized and analyzed with their respective solutions. Finally, a case study is introduced to verify the corresponding solutions and proves that the proposed method is able to secure the distributed control system while ensuring the control quality. Numerical results show the effectiveness and feasibility of the proposed approach.

In this article, the approach of an adjustable uncertainty set is proposed to deal with the uncertainty of renewable energy (RE) in unit commitment (UC). Demand response (DR) is co-optimized to reduce the operation risk of load shedding and RE curtailment when the RE falls out of the adjustable uncertainty set. In comparison with existing approaches with an adjustable uncertainty set, the proposed approach further incorporates DR requires no predefined parameters to constrain the deviation from the forecast RE. It divides the maximum RE set into subintervals, and bounds of the adjustable uncertainty set are determined among these subintervals with the consideration of DR in reducing the operation risk. The original mixed-integer nonlinear problem of UC scheduling is transformed to be a mixed-integer linear problem to be effectively solved. The performance of the proposed approach is verified on the IEEE 6-bus, 30-bus, and 300-bus systems. Through the comparison with existing methods, the effectiveness of the proposed approach in reducing the conservativeness is verified. The effectiveness of the proposed approach in the reduction of the operation risk of load shedding and RE curtailment is verified through the comparison between situations with and without DR.

Local energy trading in microgrids is one of the emerging concepts in the area of distribution networks. A proper business model is required to manage local energy trading. The pricing mechanism is crucial because the agreed energy price determines the benefits of local energy trading. Designing a proper pricing mechanism with a specific objective considering the privacy of agents and respecting physical network constraints is a challenging task. This article proposes a decentralized algorithm for local energy trading in microgrids with an integrated pricing mechanism considering welfare maximization and network voltage management through local information exchange among neighbors. The proposed algorithm guarantees that the energy transactions do not violate voltage constraints in a physical network and agents' privacy is preserved. A two-stage approach is proposed to achieve fast convergence and increase the practicability of the algorithm. The simulation results are presented to verify the effectiveness of the proposed approach.

Distributed generation in the microgrid becomes increasingly significant, as it eliminates the power losses from long-distance electricity transmission lines. Distributed energy resources owners who can both produce and consume energy are defined as prosumers. To encourage the peer-to-peer (P2P) energy trading between prosumers, blockchain as a thriving technology is utilized in the P2P network due to its transparency, security, and rapidity in executing transactions. Due to its decentralized quality, any intermediaries are eliminated so that transactions happen directly among traders. This article introduces a consortium blockchain trading model to support P2P energy trading, using a proof-of-stake protocol. The pre-selected miners are responsible for compensating the power losses in distribution lines by energy transactions. The specific process of the blockchain establishment, as well as the smart contract creation, are demonstrated. In addition, a type of crypto-currency named “elecoin” is created in the P2P market, which is published by the mining mechanism of the blockchain. Finally, a case study is introduced to realise the functions of the proposed blockchain model. Simulation results show the feasibility and effectiveness of the proposed approach.

The increasing penetration of renewable energy sources (RESs) has led to the proliferation of microgrids (MGs) in the power system. Recently, the concept of multimicrogrid (MMG) systems has come into prominence due to the economic benefits accrued through the sharing of resources between the constituent MGs. The uncertainties caused by the penetration of RESs necessitate the usage of advanced optimization procedures to manage the MMG system. This article proposes a robust optimization (RO) framework to determine the day-ahead schedule of an MMG system. Unlike the existing works in the literature, the proposed RO framework preserves the nonanticipativity in reserve scheduling. The proposed RO framework also includes a cooperative bidding-based trading scheme to facilitate the sharing of energy and reserves between the constituent MGs in the MMG system. The results highlight the economic benefits obtained through the sharing of resources between the constituent MGs in an MMG system. Furthermore, the results also demonstrate that the proposed nonanticipative RO framework performs better in terms of robustness when compared with the existing RO frameworks for MMG systems in the literature.

In this paper, a dynamic evolution control for the operation of the three-level bidirectional DC-DC converter with supercapacitor system is presented. The three-level DC-DC converter configuration is effective in reducing the stress on the power semiconductors switches and provides better performance with smaller passive components compared to two-level DC-DC converters. The proper control method is inevitable for its effective operation. The dynamic evolution control method requires only two controller parameters to be tuned for the operation of the three-level bidirectional DC-DC converter. In the dynamic evolution control method, the voltage balance control is also included in the control equation which makes its simple to design. Real-time simulation studies validate the effectiveness of the proposed control method.

Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand-supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors, such as driver behavior, location of charging stations, electricity pricing, etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors that influence the charging demand of EVs. Several studies have modeled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This paper aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using NetLogo is employed in this paper to closely mimic the human aggregate behavior and its influence on the load demand due to charging of EVs.

With the proliferation of enabling smart grid technologies, industries are increasingly looking to reduce their electricity costs through the adoption of renewable energy sources and efficient load management strategies. In this context, the realization of lower electricity costs requires the design of efficient energy management systems (EMSs). An EMS needs to factor in the unique operational requirements of the industry for which it is designed. Consequently, there has been a lot of research interest in designing EMSs for various industrial applications. However, the existing EMS formulations and models are not suitable for a shipyard drydock. Shipyard drydocks may be treated as grid-connected microgrids containing pump loads, interruptible loads, critical loads and heterogeneous generation sources. This paper proposes three modules, which can constitute a shipyard drydock energy management system (SEMS). A load forecasting module generates short term and medium term load forecasts using historical load demand data and ship arrival schedules as inputs. A multilayer feed-forward neural network with backpropagation is used to perform the load forecasting. A contracted capacity optimization module uses the medium term load forecast to find the optimal contracted capacity for the drydock. The short term load forecast and the optimal contracted capacity are used by an optimal scheduling module to reduce the total electricity cost incurred by the shipyard drydock. Case studies performed using data from a local shipyard demonstrate the efficacies of the proposed load forecasting and optimal scheduling modules in improving the accuracies of the load forecasts and reducing the overall electricity cost, respectively.

The uncertainty associated with solar photo-voltaic (PV) power output (PO) is a big challenge to design, manage and implement effective demand response, and management strategies. Therefore, an accurate PV power output forecast is an utmost importance to allow seamless integration and a higher level of penetration. In this research, a neural network ensemble (NNE) scheme is proposed, which is based on particle swarm optimization trained feedforward neural network (FNN). Five different FFN structures with varying network complexities are used to achieve the diverse and accurate forecast results. These results are combined using trim aggregation after removing the upper and lower forecast error extremes. Correlated variables namely wavelet transformed historical PO of PV, solar irradiance, wind speed, temperature, and humidity are applied as inputs to the multivariate NNE. Clearness index is used to classify days into clear, cloudy, and partial cloudy days. Test case studies are designed to predict the solar output for these days selected from all seasons. The performance of the proposed framework is analyzed by applying training dataset of different resolution, length, and quality from seven solar PV sites of the University of Queensland, Australia. The forecast results demonstrate that the proposed framework improves the forecast accuracy significantly in comparison with individual and benchmark models.

The transient stability control of power systems with growing penetration of renewable energy resources is challenging due to inherent small damping of generators and complicated operating conditions. To address the drawbacks of existing control approaches which need accurate systemwide network parameters, a model-free fuzzy controller is proposed to enhance the transient and frequency stability of power systems. Also, an adaptive parameter estimation scheme is developed to eliminate the fuzzy approximation errors and compensate the external disturbances. The proposed strategy is implemented based on the multiagent framework, which enables the sharing of communication and computation burdens among local controllers for fast and coordinated response. The convergence of the proposed distributed control approach is rigorously proved using the Graph theory and Lyapunov stability theory. Simulation studies validate the effectiveness of the proposed distributed control approach.

This paper presents the topology, analysis, and control implementation of a new single-phase flexible power quality conditioner (FPQC), which can be used for handling voltage- and current-related power quality (PQ) problems. Different from other PQ-compensating devices, the FPQC can work under two compensating modes, i.e., parallel-connection mode (PCM) and series-connection mode (SCM). When the source voltage is unpolluted, the FPQC is working under the PCM so that the load harmonic current and reactive power can be compensated. When the source voltage is polluted, the FPQC is switched to the SCM to mitigate the impact caused by the polluted voltage. In this way, a desired voltage can be maintained at the load side to protect the critical load. The mode switching between PCM and SCM is realized by two relays. To ensure fast dynamics response and smooth transient operation, the model predictive control method is applied to regulate the developed FPQC. Both the simulation platform and laboratorial prototype are built to validate the effectiveness of the system. The simulation and experimental results are presented and discussed.

The maximum current carrying capacity of a power cable (Ampacity) is determined by the thermal characteristics of the cable components and surrounding medium in which they are buried. Power cable ampacity calculations are based on typical standard tables defined with predetermined parameters. In realtime, the environment of installation plays significant role in cable current carrying capacity. This paper presents FEM approach to determine the maximum current capacity within the safe operating limits of the cable by modeling electro-magnetic heat transfer. The study includes complex thermo-electric coupling and heat transfer in different zones surrounding the cable and its effect on the conductor operating temperature. The results show the difference between the proposed approach and standard calculations and improvements to accurate rating of the cables.

Microgrid (MG) represents one of the major drives of adopting Internet of Things for smart cities, as it effectively integrates various distributed energy resources. Indeed, MGs can be connected with each other and presented as a system of multimicrogrid (MMG). This paper proposes the optimal operation of MMGs by a cooperative energy and reserve scheduling model, in which energy and reserve can be cooperatively utilized among MMGs. In addition, values of Shapely are introduced to allocate economic benefits of the cooperative operation. Finally, a case study based on a system of MMGs is conducted, and simulation results verify the effectiveness of the proposed cooperative scheduling model.

As one of the most promising solutions to mitigate the power quality (PQ) problem of the modern power systems, the unified PQ conditioner (UPQC) draws considerable attentions. Since the UPQC consists of two sets of power converters, it will greatly increase the manufacturing investment of the setup. In this paper, the optimal volt-ampere (VA) ratings of the converters in the UPQC are investigated due to system compensating requirements. The phase angle control (PAC) method is discussed and illustrated to have the feature of changing the online VA loading by adjusting the corresponding displacement angle. On the basis of the variable PAC method, a two-stage algorithm is utilized to optimize the ratings of the shunt and series converters in order to obtain the maximum utilization rates of the power converters in the UPQC. Moreover, the corresponding control algorithm is utilized to reduce the proposed UPQC online VA loadings for the different compensating operations. The proposed UPQC is compared with other approaches to highlight the advantage of the proposed optimization algorithm. The proposed algorithms are also validated with the simulation and the real-time control hardware-in-loop results of the designed system.

Large scale adoption of electric vehicles (EVs) would significantly increase the overall electricity demand of the power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand-supply mismatch. Accurately predicting the realistic charging demand of energy storage systems (ESS) used in EVs is an essential part of the infrastructure planning. Charging demand of ESS used in EVs is affected by several factors such as driver behavior, location of charging stations and electricity pricing. In order to implement the optimal charging infrastructure, it is important to consider all the crucial factors that affect the charging demand of ESS in EVs. Several studies have modelled and simulated the charging demand of individual as well as group of EVs. However, in many cases the models did not include factors that deal with the social characteristics of EV drivers, while the others did not emphasise on the economic elements. This paper aims to evaluate the effects of above factors on the EV charging demand using a simulation model. Agent-based approach using NetLogo is employed in this study to closely mimic the human crowd behaviour and its influence on the load demand due to charging of ESS used in EVs.

Stationary Battery Systems (SBS) are becoming a critical component in power distribution network across the world. Penetration of renewable energy sources which are intermittent in nature is a huge influence on the requirement of SBS. Furthermore, SBS are used in other applications such as peak load management, load-shifting, voltage regulation and power quality improvement. With increase in penetration on SBS, the requirement for modeling charging characteristics considering capacity loss is also increasing drastically. Minimal resource requirement and capability to leverage on smart meter data are the important parameters that are to be focused while developing any model for such applications. In this paper, an analysis on different curve fitting approaches that can be used for predicting the charging profiles of SBS based on lithium iron phosphate batteries is presented.

This paper presents a new approach for the determination of underground cable ampacity that considers surrounding medium of the cable, and compares the results from the FEM model with standard approach. It shows how ampacity of a buried cable is affected by the extent of heat transfer from the cable to the surrounding soil and also by the heterogeneity of the soil and its thermal characteristics. Numerical and finite element model of steady-state thermal analysis and ampacity evaluation are presented in this paper. COMSOL software is used for the three dimensional simulation of a 44kV armored HVAC XLPE cable buried directly in native soil. The methodology includes mathematical solutions for heat transfer equations to calculate and obtain the temperature at the cable surface and results show the optimal acceptable ampacity. The results from the proposed method provide solutions for ampacity problems that require flexibility and dynamic approach for real life scenarios that are not present in the literature and previous works.

In this paper, a new control approach is proposed for PV system with hybrid energy storage system (HESS) in isolated DC grid application. The proposed control approach solves the current controller conflict problem in HESS and provides faster DC link voltage restoration. In the proposed control approach a predictive term is used to control the battery current and the supercapacitor (SC) current. In the proposed control approach the voltage error term and the uncompensated power from the battery is added to the supercapacitor current reference to achieve faster DC link voltage restoration and less stress in the battery system. The system parameters design and closed loop system stability analysis of the proposed control approach are discussed in detail in the paper. The effectiveness of the proposed control approach is verified by simulation studies.

This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.

Online optimal control of a multicarrier home energy system, including a fuel cell (FC) with combined heat and power functionality, a furnace, and a battery as an energy storage system, is presented in this paper. An appropriately defined objective function (OF) is formulated to address the operation costs of the home energy system by considering the electrical time-of-use price and time-varying electrical and thermal demand. To determine the optimal control strategy of the FC, the defined OF is solved analytically to reach a closed-form solution for the optimal operation of the FC, which results in the global optimum compared with the near-optimal point in previous works of FC scheduling. To attain the whole system optimal operation, a rule-based solution for the optimal operation of the battery is proposed according to its characteristics. The proposed method is applied to a realistic case study including real load demand and experimentally verified data of the FC. The results are compared with previous approaches, in which optimization techniques have been used to achieve near-optimal scheduling of FC with/without a battery. It is shown that the proposed control strategy results in more exact scheduling and can be applied successfully in real applications.

This paper presents a solar Photovoltaic (PV) inverter along with a battery energy storage device in shunt with a three-phase grid. Apart from sharing the load active power, the other objective of the PV-battery integrated system is to provide load harmonic and reactive power compensation throughout the day. The interface between the grid and the PV is carried out through a voltage source converter. The voltage across the DC bus capacitor is regulated to maintain a unity power factor operation of the system, irrespective of changes in solar radiation level or due to change in load. A bidirectional DC-DC converter is employed to charge and discharge the connected battery energy storage device. The performance of the system is analyzed with and without the battery, in order to highlight the importance of battery energy storage system.

This paper presents a new concept of co-ordinated active power sharing between shunt and series converters of unified power quality conditioner (UPQC) for distributed generation applications. Normally, the UPQCs are used to mitigate both voltage and current power quality problems. However, these UPQCs are also used for delivering active power in addition to its power quality improvement by integrating distributed generation (DG) at the DC link of back to back connected converters. But, only the shunt converters are used to carry the whole active power from the DG sources and the series converters are used to handle only voltage related power quality problems. So, the shunt converter is loaded heavily and the series converter is kept idle in steady state cases. The more dependency on the shunt converter also reduces the reliability of the total system. This proposed control strategy is used to carry active power through both series converter and shunt converter even at the steady state conditions. The proposed method improves the utilization of the converters and also the reliability of the system. The effectiveness of the proposed control strategy is demonstrated by comparing with the conventional control algorithm, where only the shunt converter is used to carry active power. The proposed system is validated by performing hardware in loop (HIL) tests using OPAL-RT and dSPACE DS1103.

In this paper, a unified adaptive energy management scheme (EMS) is proposed for renewable-interfaced hybrid energy storage system (HESS) under grid connected/islanded conditions. A second harmonic based phased locked loop is employed for effective synchronization/resynchronization of the microgrid system under contingency conditions. The operation and management of the microgrid system under both these modes are accomplished by an efficient adaptive power management algorithm. A quantitative analysis on the HESS performance is provided in order to investigate the effectiveness of the proposed approach. Load curtailment and off-maximum power point tracking features are also accommodated in the proposed scheme. This approach address seamless transfer between the various sub-modes of the system along with additional services such as power quality enhancement and effective power dispatch between various sources. The effectiveness of the proposed scheme is verified by both simulation and experimental investigations.

This paper proposes a dual topology modes neutral-point-clamped grid-connected inverter (DTM-NPCI) suitable for the photovoltaic generation system. Two bidirectional power switches are plugged into the single-phase cascaded NPC inverter (CNPCI) to connect the positive poles and negative poles of the two NPC inverters (NPCI) respectively to achieve the switching between the CNPCI and the single NPCI. The system works in CNPCI or NPCI according to the actual output power and voltage of the PV array to achieve a wide operational range and high European efficiency. Taking that the grid current THD meets the specific grid-connected standard in the entire operational range as the requirement, the operational ranges of the two topology modes are determined. The experimental results of the proposed DTM-NPCI are shown to validate the accuracy and feasibility.

This paper proposes a modified cascaded multilevel grid-connected inverter (MCM-GCI) suitable for photovoltaic grid-connected generation system, which considers wide operation range, low grid current total harmonics distortion (THD), and high European efficiency. In the proposed topology, a bidirectional power switch is added to the standard single-phase cascaded multilevel inverter (CMI) to implement the transformation between CMI mode and H-bridge inverter (HBI) mode. An online topology transformation approach of the MCM-GCI is proposed to guarantee the topology modes transformed safely and smoothly. The proposed MCM-GCI operates in CMI mode when the PV arrays' output voltage and power are low, and transforms into HBI mode when the PV arrays' output voltage and power are high. Experimental results of five-level MCM-GCI are represented to validate the feasibility of the proposed topology and it is also compared with other three classic grid-connected inverters to highlight its advantages. Furthermore, more structures based on hybrid CMI, which possess the topology transformation ability, are shown to further improve the generation range and obtain a higher efficiency of the system.

Regenerative braking improves the energy efficiency of railway transportation by converting kinetic energy into electric energy. This paper proposes a method to apply the Bellman-Ford (BF) algorithm to search for the train braking speed trajectory to increase the total regenerative braking energy (RBE) in a blended braking mode with both electric and mechanical braking forces available. The BF algorithm is applied in a discretized train-state model. A typical suburban train has been modeled and studied under real engineering scenarios involving changing gradients, journey time, and speed limits. It is found that the searched braking speed trajectory is able to achieve a significant increase in the RBE, in comparison with the constant-braking-rate (CBR) method with only a minor difference in the total braking time. An RBE increment rate of 17.23% has been achieved. Verification of the proposed method using BF has been performed in a simplified scenario with zero gradient and without considering the constraints of braking time and speed limits. Linear programming (LP) is applied to search for a train trajectory with the maximum RBE and achieves solutions that can be used to verify the proposed method using BF. It is found that it is possible to achieve a near-optimal solution using BF and the solution can be further improved with a more complex search space. The proposed method takes advantage of robustness and simplicity of modeling in a complex engineering scenario, in which a number of nonlinear constraints are involved.

Deployment of a hybrid wireless and local area network (LAN) system is a feasible solution for smart grid communication. In this paper, a combination of fixed worldwide interoperability for microwave access (WiMAX) at 5.8 GHz and Ethernet LAN is proposed for monitoring and control of energy meters in a smart grid. Communication signal quality is an important factor to consider as it directly affects the reliability of the whole monitoring and control system of a smart grid. In this paper, the signal quality in terms of received signal strength indicator (RSSI) of the WiMAX system is investigated with a laboratory-scale smart grid prototype. Real-time RSSI is recorded and analyzed to develop an analytical model for the WiMAX RSSI transmitting at 5.8 GHz. It was found that in order to achieve reliable communication for a coverage area of 2 km, the base station has to transmit at a total power of 28.08 dBm for a receiver height of 1.875 m. Similarly, a receiver height of 2.766 m is required for a transmitted power of 25 dBm. The performance of the proposed WiMAX-LAN communication system for advanced metering infrastructure in smart grids is analyzed. On average, the proposed system completes the data collection of 3705 meter units in less than 997 s.

This paper describes an automated curriculum timetabling system based on a stochastic search methodology, namely a co-evolutionary algorithm. The application timetable is taken from the undergraduate courses of the School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU). A co-evolutionary algorithm approach is found to be well suited. Practical courses have duration greater than one hour. A schedule can be generated separately and its population, which consists of a set of practical schedules, is termed as the practical population. Lecture and tutorial schedules can also be generated separately. These are of one-hour duration and they are termed collectively as lecture/tutorial schedule. A set of lecture/tutorial schedules could be generated to form the lecture/tutorial population. These two populations use the same set of resources and have constraining effects upon one another. Since the placement of practical courses have a more constraining effect, the schedules in the practical population are first generated and are then used to guide the generation of the set of lecture/tutorial schedules. For every lecture/tutorial schedule generated, it is combined with its corresponding practical schedule to form a combined schedule. The average fitness of all the combined schedules is then computed and used as a measure of the fitness of the practical schedule that drives them. The practical population is then evolved progressively to obtain the best practical schedule. It is then used as a base configuration for the rest of the courses to populate and evolve. The resultant system compares favorably to the current manual system.