This study proposes an electric vehicle (EV) aggregator operation mechanism in a residential community. The EV charging and discharging operation behaviours are scheduled to maximise the EV aggregator revenue, while EV aggregator provides reserve service for the grid. This study not only considers the energy and information interactions between three stakeholders: EV aggregator, EV owners, and power grids, but also the economic interests of aggregator and owners are considered. The aggregator-owner economic inconsistency issue (EV owners get higher charging cost in aggregator scheduling than self-scheduling) is presented. In order to mediate this issue, a rebate factor is proposed. In the first stage, the objective is to minimise the day-ahead (DA) charging cost of EV owners. Then the second stage is to maximise DA aggregator revenue with different rebate values. Finally, in the third stage, a real-time scheduling strategy is proposed to maximise aggregator revenue using the optimal rebate value. In addition, the battery degradation in influencing scheduling is formulated. Scheduling results show the effectiveness of the proposed strategy, e.g. economic inconsistency of different parties can be mediated. Significant reduction of EV owners’ cost from self-scheduling can be achieved while the revenue of EV aggregator is maximised under the proposed strategy.

Dynamic energy pricing policy introduces real-time power-consumption-reflective pricing in the smart grid in order to incentivise energy consumers to schedule electricity-consuming applications (tasks) more prudently to minimise electric bills. This has become a particularly interesting problem with the availability of photovoltaic (PV) power generation facilities and controllable energy storage systems. This study addresses the problem of concurrent task scheduling and storage management for residential energy consumers with PV and storage systems, in order to minimise the electric bill. A general type of dynamic pricing scenario is assumed where the energy price is both time-of-use and power dependent. Tasks are allowed to support suspend-now and resume-later operations. A negotiation-based iterative approach has been proposed. In each iteration, all tasks are ripped-up and rescheduled under a fixed storage charging/discharging scheme, and then the storage control scheme is derived based on the latest task scheduling. The concept of congestion is introduced to gradually adjust the schedule of each task, whereas dynamic programming is used to find the optimal schedule. A near-optimal storage control algorithm is effectively implemented. Experimental results demonstrate that the proposed algorithm can achieve up to 60.95% in the total energy cost reduction compared with various baseline methods.

This paper investigates a service level agreements (SLAs)-based resource allocation problem in a server cluster. The objective is to maximise the total profit, which is the total revenue minus the operational cost of the server cluster. The total revenue depends on the average request response time, whereas the operating cost depends on the total energy consumption of the server cluster. A joint optimisation framework is proposed, comprised of request dispatching, dynamic voltage and frequency scaling (DVFS) for individual cores of the servers, as well as server- and core-level consolidations. Each DVFS-enabled core in the server cluster is modelled by using a continuous-time Markov decision process (CTMDP). A near-optimal solution comprised of a central manager and distributed local agents is presented. Each local agent employs linear programming-based CTMDP solving method to solve the DVFS problem for the corresponding core. On the other hand, the central manager solves the request dispatch problem and finds the optimal number of ON cores and servers, thereby achieving a desirable tradeoff between service response time and power consumption. To reduce the computational overhead, a two-tier hierarchical solution is utilized. Experimental results demonstrate the outstanding performance of the proposed algorithm over the baseline algorithms.

Embedded control parameters of cyber-physical systems (CPS), such as sampling rate, are typically invariant and designed with a worst case scenario in mind. In an over-engineered system, control parameters are assigned values that satisfy system-wide performance requirements at the expense of excessive energy and resource overheads. Dynamic and adaptive control parameters can reduce the overhead but are complex and require in-depth knowledge of the CPS and its operating environment – which typically is unavailable during design time. The authors investigate the application of reinforcement learning (RL) to dynamically adapt high-level system parameters, at run time, as a function of the system state. RL is an alternative approach to the classical control theory for CPSs that can learn and adapt control properties without the need of an in-depth controller model. Specifically, we show that RL can modulate sampling times to save processing power without compromising control quality. We apply a novel statistical cloud-based evaluation framework to study the validity of our approach for the cart-pole balancing control problem as well as the well-known mountain car problem. The results show an improved real-world power efficiency of up to 20% compared with an optimal system with fixed controller settings.

Smart grid cyber-physical system (CPS) exploits various physical components to provide better knowledge and delicate control of the power grid, while the huge data volume is transmitted via the integration of advanced communication technologies. To provide better services for the applications in the smart grid CPS, the communication network has to consider the aspects of both improving the system throughput and meeting the real-time requirement. In order to address this issue, a new communication strategy is proposed in this study. The strategy is based on the time performance features of different smart grid CPS applications, which also exploits both temporal and spatial available spectrum resources for transmitting via spectrum sharing techniques. Moreover, the performance has been verified by a case study based on IEEE 14-bus power system. An important real-time application, namely real-time voltage stability enhancement, has been investigated in the case study. Results show that the proposed communication strategy is able to improve the throughput of the smart grid CPS and the time performance of time sensitive applications.

An ongoing evolution of the power grids into more intelligent and sophisticated ones has been taking place since the beginning of the 21st century. The underlying objective of the power systems is to deliver electrical energy with high-security standards, i.e. to supply power to the consumers uninterruptedly. However, the integration of information technology into the smart grid introduces new vulnerabilities related to cyber-security which the authors should address extensively. This study discusses the impact of coordinated cyber-attacks on the advanced metering infrastructure. In this work, emulations of distributed denial-of-service attacks in a closed testbed environment using a topology of smart meters that participate in an electricity market are being performed. This study proposes a method to evaluate the impact on the reliability of such attacks. The results demonstrate that the proposed method can serve as a tool for the evaluation of the short-term risk of botnet attacks during load shifting in smart distribution networks.