• Energy Research

A programmable intrusion detection method is presented to identify the malicious attacks to distributed energy resources (DERs) in the cyber–physical networked microgrids. The proposed method injects small programmable signals into the system and uses the response to identify abnormal conditions. Because of the low or even zero inertia induced by integrations of DER power-electronic-interfaces, microgrids have very limited resilience capability; and thus, being sensitive to attacks. One microgrid’s malfunction caused by attacks can easily propagate to its neighboring systems when several microgrids are connected, leading to catastrophic electricity supply failures. Through the presented method, malicious intrusions can be effectively detected, located, and defended for securing microgrids. Theoretical derivations are provided to define the programmable detection rules. The detection rule is easy and flexible to update, making it difficult for attack actors to gain the knowledge of the detection rules, in order to avoid being detected. Numerical results on a cyber–physical networked microgrids system show that the proposed method is effective and efficient in precisely locating intrusion attacks to the microgrids system.

AbstractSmart local energy systems (SLES) have been reported in the past decade, which are associated with diverse energy carriers, components and objectives. This paper provides a comprehensive review of information and communications technology (ICT) infrastructure of SLES. A systematic survey of existing research work and industrial projects was provided to highlight, categorise and analyse the ICT infrastructure, which lays the foundation for the successful functioning of SLES. First of all, various SLES measurements are described and categorised based on the energy carriers and technologies. Then, communications infrastructure for SLES is described with communications technologies summarised. Moreover, the ICT infrastructures for SLES are categorised and summarised based on their objectives and technologies. Finally, the challenges and recommendations are presented. The findings from this paper are intended to serve as a convenient reference for developing future SLES.

AbstractA robust optimization model was proposed for smart home load scheduling to tackle the uncertain challenges brought by PV system, in which an adaption parameter, Γ, is defined to control the robust level of the final optimal solution. The proposed robust optimization is capable of producing load schedules with different electricity cost and robust levels. Final decisions can be made as a tradeoff according to users’ financial situation and risk preference.

A novel two-level scheduling method was proposed in this paper, which helps an aggregator optimally schedule its flexible thermostatically controlled loads with renewable energy to arbitrage in the intraday electricity market. The proposed method maximizes the economic benefits of all the prosumers in the aggregation, and naturally helps balance intra-hour differences between supply and demand of the bulk power systems because the prices of the intraday electricity market reflects the need of the bulk power systems. In the proposed two-level scheduling, the upper level is a model predictive control optimization, of which the objective function is to minimize the sum of energy and capacity cost of imbalances and the constraints are thermal constraints based on a proposed energy-balanced model, while the lower level adopts the typical temperature priority list (TPL) control. Simulation results verified the validity of the proposed method and evaluated the effects of important influencing factors. In the base case, 41.64% imbalance cost was saved compared to the reference TPL-based control. Moreover, three further conclusions were drawn: (a) the proposed method mainly saves the imbalance cost by reducing imbalance peak, thus being suitable for places with high capacity price for imbalances; (b) parameter heterogeneity affects the performance of the proposed method, and average value method performs well only with low heterogeneity; (c) the performance of the proposed method worsens with the increase of forecast uncertainty, but keeps better than that of typical TPL-based control unless the forecast uncertainty gets very strong.

For distributed solid electricity thermal storage aggregators (DSETSA), the uncertainty of the marginal clearing price may lead to the problem of multibidding scenarios (including successful, part successful, and failed biddings) in the electricity spot market. Moreover, the marginal operating cost affecting the bidding revenue in the spot market is not considered in the existing methods, which challenge the bidding of the aggregators. To address the challenge, this article proposes an optimized operation framework for DSETSA. First, based on the incomplete information characteristics of the spot market, an optimal bidding model which incorporates marginal operating cost constraints for DSETSA under multibidding scenarios is proposed to increase the operation profit. Second, the DSETSA's multibidding scenario problem induced by the uncertainty of the marginal clearing price in the electricity spot market is cast into a probability distribution representation using the Bayesian incomplete information theory to increase the chances of winning bids. Finally, framework establishes the relationship between the bidding price, electricity demand, and public traded electricity in the spot market. The effectiveness of the proposed framework is demonstrated through simulations.

To reduce the consumption of fossil fuel and greenhouse gas (GHG) emissions, incentive-based policies are used to encourage end-users to utilize more clean energy. Hydrogen energy is an ideal clean energy that can be integrated into the next generation power grid. Deferrable electrolyzers (DEs), as a typical electricity-to-hydrogen conversion devices and capable of modulating power consumption, can convert excessive power to store electricity as hydrogen. Therefore it can be used as a method for load management. The main contribution of this paper is to propose an energy-constrained state priority list (ECSPL) model, for analyzing the charging response of aggregated loads consisting of DE units. The typical hysteresis control of DEs as a load management mechanism is first discussed. A characteristic parameter, i.e. the energy state of DE charging load, is used to group and prioritize the DE units. The proposed ECSPL model optimally determines the operating status of DE charging and standby process, and it maintains the user-desired DE charging trajectory considering customer-constraints. The proposed model maintains the diversity of operating status of DE charging and standby process to prevent unexpected synchronization phenomenon for operating status. To evaluate the performance of the proposed method, an estimated baseline of the aggregated DE charging loads is obtained based on natural hysteresis control. The ECSPL control method of DE units for intra-hour load balancing is then evaluated. The effects of different energy-constraints, deadbands of sampled end-use state comparison, error associated with the charging-trajectory measurements are modeled to evaluate the performance of controlled DE group. The ECSPL model is described and demonstrated by the modeling results of investigated DE units.

This paper presents a demand response (DR) and battery storage coordination algorithm for providing microgrid tie-line smoothing services. A modified coordinating control strategy is implemented through two-way communication networks to manage distributed heat pumps in a microgrid for smoothing the tie-line (connect the microgrid to the main grid) power fluctuations. A total of 1000 residential electric heat pumps and a battery storage system are modeled to demonstrate the effectiveness and robustness of the proposed algorithm. The impact of outdoor temperature changes and customer room temperature preferences is considered in the simulation. The results show that coordinating with DR programs can significantly reduce the size of conventional energy storage systems for large-scale integration of renewable generation resources in microgrids and improve the power quality.