Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties

Scheduling of prosumer flexibility is challenging in finding an optimal allocation of energy resources for heterogeneous prosumer goals under various forecast uncertainties and operation constraints. This study addresses this challenge by introducing a bottom-up framework for cooperative flexibility scheduling that relies on a decentralized network of scheduling agents to perform a coordinated decision-making and select a subset of households’ net load schedules that fulfills the techno-socio-economic prosumer objectives in the resource operation modes and ensures the reliability of the grid. The resource flexibility in terms of alternative operation schedules is mathematically modeled with multiobjective optimization that attains economic, environmental, and energy self-sufficiency prosumer goals with respect to their relative importance. The coordination is achieved with a privacy-preserving collective learning algorithm that aims to reduce the aggregated peak demand of the households considering prosumers’ willingness to cooperate and accept a less preferred resource schedule. By utilizing the framework and real-world data, the novel case study is demonstrated for prosumers equipped with solar battery systems in a community microgrid. The findings show that the flexibility scheduling with an optimal prosumer cooperation level decreases the global costs of collective peak shaving by 83% while increasing the local prosumer costs by 28% in comparison with noncooperative scheduling. However, the forecast uncertainty in net load and parameters of the frequency containment reserve causes imbalances in the planned schedules. It is suggested that the imbalances can be decreased if the flexibility modeling takes into account variable specific levels of forecast uncertainty

A Shrinking Horizon Model Predictive Controller for Daily Scheduling of Home Energy Management Systems

In this paper, the model predictive control (MPC) strategy is utilized in smart homes to handle the optimal operation of controllable electrical loads of residential end-users. In the proposed model, active consumers reduce their daily electricity bills by installing photovoltaic (PV) panels and battery electrical energy storage (BEES) units. The optimal control strategy will be determined by the home energy management system (HEMS), benefiting from the meteorological and electricity market data stream during the operation horizon. In this case, the optimal scheduling of home appliances is managed using the shrinking horizon MPC (SH-MPC) and the main objective is to minimize the electricity cost. To this end, the HEMS is augmented by the SH-MPC, while maintaining the desired operation time slots of controllable loads for each day. The HEMS is cast as a standard mixed-integer linear programming (MILP) model that is incorporated into the SH-MPC framework. The functionality of the proposed method is investigated under different scenarios applied to a benchmark system while both time-of-use (TOU) and real-time pricing (RTP) mechanisms have been adopted in this study. The problem is solved using six case studies. In this regard, the impact of the TOU tariff was assessed in Scenarios 1-3 while Scenarios 4-6 evaluate the problem with the RTP mechanism. By adopting the TOU tariff and without any load shifting program, the cost is $\$$ 1.2274 while by using the load shifting program without the PV and BEES system, the cost would reduce to $\$$ 0.8709. Furthermore, by using the SH-MPC model, PV system and the BEES system, the cost would reduce to $\$$ -0.282713 with the TOU tariff. This issue shows that the prosumer would be able to make a profit. By adopting the RTP tariff and without any load shifting program, the cost would be $\$$ 1.22093 without any PV and BEES systems. By using the SH-MPC model, the cost would reduce to $\$$ 1.08383. Besides, by adopting the SH-MPC, and the PV and BEES systems, the cost would reduce to $\$$ 0.05251 with the RTP tariff, showing the significant role of load shifting programs, local power generation, and storage systems

Energy Internet: Cyber-physical Deployment of Future Distribution Grids

Energy Internet is a concept broadly used by researchers and other practitioners indicating the increased use of information and communication technologies (ICTs) in the management of decentralized electric power grids with distributed energy resources. The Energy Internet is conceptually similar to the (Data) Internet (The Economist 2004). More precisely, the Energy Internet refers to a large-scale cyber-physical system built upon packetized energy management of flexible loads in single or networked microgrids, enabled by the advances in ICTs, especially machine-type communications (Nardelli et al. 2019).Post-print / Final draf

Community Renewable Energy Systems

-Post-print / Final draf

Maximising transmission capacity of ad hoc networks via transmission system design

The trade-off involving capacity and interference in wireless networks, using the metric transmission capacity, is investigated. Particularly, it is shown that this metric appropriately captures the effects of this trade-off, and can be used to select the combination of modulation scheme/coding scheme that maximises the transmission efficiency of a wireless ad hoc network modelled as a homogeneous Poisson point process.475348U8

Factors Influencing the Adoption of m-Government: Perspectives from a Namibian Marginalised Community

Mobile-government (m-Government) services adoption is being advanced as an alternative solution for addressing challenges faced by electronic-government (e-Government) adoption in marginalised communities. However, factors of m-Government need to be understood if it is to be adopted by marginalised communities. There are suggestions that many contextual factors affect to the adoption of m-Government services. In this study, factors of m-Government in Oniipa, a marginalised rural community in Namibia are researched. Results show that security, technology trust, ICT supporting infrastructure, usage experience, costs, awareness, skills for accessing m-Government, language literacy, training, perceived ease of use, perceived usefulness, social influence, perceived empathy and compatibility are critical factors of m-Government services adoption. The study findings shall be used to propel m-Government adoption in a Fusion Grid project that aims to address infrastructural challenges faced by marginal communities when adopting e-Government. Similarly, policy makers can draw lessons on m-Government adoption from this study.Peer reviewe