Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible.

Model predictive control (MPC) schemes employ dynamic models of a process within a receding horizon framework to optimize the behavior of a process. Although MPC has many benefits, a significant drawback is the large computational burden, especially in adaptive and constrained situations. In this paper, a computationally efficient self-tuning/adaptive MPC scheme for a simple industrial process plant with rate and amplitude constraints on the plant input is developed. The scheme has been optimized for real-time implementation on small, low-cost embedded processors. It employs a short (2-step) control horizon with an adjustable prediction horizon, automatically tunes the move suppression (regularization) parameter to achieve well-conditioned control, and presents a new technique for generating the reference trajectory that is robust to changes in the process time delay and in the presence of any inverse response. In addition, the need for a full quadratic programming procedure to handle input constraints is avoided by employing a quasi-analytical solution that optimally fathoms the constraints. Preliminary hardware-in-the-loop (HIL) test results indicate that the resulting scheme performs well and has low implementation overhead.

In this paper, it is argued that some low-level aspects of the usual IEC 61850 mapping to Ethernet are not well suited to microgrids due to their dynamic nature and geographical distribution as compared to substations. It is proposed that the integration of IEEE time-sensitive networking (TSN) concepts (which are currently implemented as audio video bridging (AVB) technologies) within an IEC 61850 / Manufacturing Message Specification framework provides a flexible and reconfigurable platform capable of overcoming such issues. A prototype test platform and bump-in-the-wire device for tunneling horizontal traffic through AVB are described. Experimental results are presented for sending IEC 61850 GOOSE (generic object oriented substation events) and SV (sampled values) messages through AVB tunnels. The obtained results verify that IEC 61850 event and sampled data may be reliably transported within the proposed framework with very low latency, even over a congested network. It is argued that since AVB streams can be flexibly configured from one or more central locations, and bandwidth reserved for their data ensuring predictability of delivery, this gives a solution which seems significantly more reliable than a pure MMS-based solution.

The use of wireless communications for real-time control applications poses several problems related to the comparatively low reliability of the communication channels. This paper is concerned with adaptive and predictive application-level strategies for ameliorating the effects of packet losses and burst errors in industrial sampled-data Distributed Control Systems (DCSs), which are implemented via one or more wireless and/or wired links, possibly spanning multiple hops. The paper describes an adaptive compensator that reconstructs the best estimates (in a least squares sense) of a sequence of one or more missing sensor node data packets in the controller node. At each sample time, the controller node calculates the current control, and a prediction of future controls to apply over a short time horizon; these controls are forwarded to the actuator node every sample time step. A simple design method for a digital Proportional Integral Derivative (PID)-like adaptive controller is also described for use in the controller node. Together these mechanisms give robustness to packet losses around the control loop; in addition, the majority of the computational overhead resides in the controller node. An implementation of the proposed techniques is applied to a case study using a Hardware in the Loop (HIL) test facility, and favorable results (in terms of both performance and computational overheads) are found when compared to an existing robust control method for a DCS experiencing artificially induced burst errors.

The advancement in battery manufacturing has played a significant role in the use of batteries as a cost-effective energy storage system. This paper proposes an optimal charging and discharging strategy for the battery energy storage system deployed for economic dispatch and supply/demand balancing services in the presence of intermittent renewables such as solar photovoltaic systems. A decision-making strategy for battery charge/discharge operations in a discrete-time rolling horizon framework is developed as a finite-input set non-linear model predictive control instances and a dynamic programming procedure is proposed for its real-time implementation. The proposed scheme is tested on controllable loads and a photovoltaic generation scenario in the premises of a sports centre, as a part of a pilot demonstration of the inteGRIDy EU-funded project. The test results confirm that the implemented stacking of the battery and optimal decision-making algorithm can enhance net saving in the electricity bill of the sports centre, and lead to corresponding CO₂ reductions.