• Energy Research

This paper presents an approach to model the transfer function of a four-conductor power-line cable for power-line communications. The model is derived from multiconductor transmission-line theory by taking into account all couplings between the conductors. The model allows one not only to determine the transfer function, but also the interference characteristics of that transmission medium. The transfer function for a straight line without branches is obtained first, and then crosstalk is derived. Finally, an N-branch network is simplified based on a generalization of the transmission matrix method. The transfer characteristics of the equivalent network are derived in a similar way as for a straight line.

With the development of distributed energy resources, the low voltage distribution network (LVDN) is supposed to be the integrator of small distributed energy sources. This makes the users in LVDNs multifarious, which leads to more complex modeling. Additionally, data acquisition could be tricky due to rising privacy concerns. These impose severe demands on control schemes in LVDNs that the classical centralized control might not be able to fulfill. To tackle this, a model-free control approach with distributed decision-making architecture is proposed in this paper. Employing statistical methods and game theory, individual users in LVDNs achieve local optimum autonomously. Comparing to conventional approaches applied in LVDNs, the proposed approach is able to achieve active control with less communication burden and computational resources. The paper proves the convergence to the Nash Equilibrium (NE) and uses player compatible relations to form the specific equilibrium. A variant of the log-linear trial and error learning process is applied in a novel “suggest-convince” mechanism to implement the proposed approach. In the case study, a 103 nodes test network based on a real Belgian semiurban LVDN is illustrated. The proposed approach is validated and analyzed with practical load profiles on the 103 nodes network. In addition to that, centralized control is implemented as a benchmark to show the performance of the proposed approach by comparing it with the classical optimization result. The results demonstrate that the proposed approach is able to achieve player compatible equilibrium in an expected way, resulting in a good approximation to the local optimum.

Applying DSM for balancing renewable energy sources (RES), makes it possible to increase energy production of RES without the need for expensive (spinning) reserve on fossil power plants. The potential of DSM has already been demonstrated for peak shaving, but not for balancing RES. This paper illustrates the general and financial potential of DSM, describes the current practice and discusses some important aspects when using DSM for balancing. To conclude, the balancing potential of domestic freezers has been analyzed.

Power system states are not accurately known ahead of real time and are subject to natural variability, which leads to uncertainty in the system. Sources of uncertainty are amongst others load, renewable energy sources and contingencies. An aggregate measure for system's uncertainty is determined in order to have a single quantified value of the uncertainty level in the system. This allows to compare performance of power system reliability management according to various reliability criteria at different uncertainty levels in a transparent manner. A case study for a 5 node test system illustrates the use of the aggregate uncertainty measure and the impact of different uncertainty levels on short term reliability management of transmission system operators according to three reliability criteria. Results of the case study illustrate that probabilistic reliability management can lead to significant improvements in performance in terms of total system cost and curtailment of non-flexible load compared to reliability management based on deterministic N-0 and N-1 criteria, especially at high uncertainty levels in heavily loaded system conditions.

This paper studies the electric vehicle (EV) charging scheduling problem to match the stochastic wind power. Besides considering the optimality of the expected charging cost, the proposed model innovatively incorporates the matching degree between wind power and EV charging load into the objective function. Fully taking into account the uncertainty and dynamics in wind energy supply and EV charging demand, this stochastic and multistage matching is formulated as a Markov decision process. In order to enhance the computational efficiency, the effort is made in two aspects. Firstly, the problem size is reduced by aggregating EVs according to their remaining parking time. The charging scheduling is carried out on the level of aggregators and the optimality of the original problem is proved to be preserved. Secondly, the simulation-based policy improvement method is developed to obtain an improved charging policy from the base policy. The validation of the proposed model, scalability, and computational efficiency of the proposed methods are systematically investigated via numerical experiments.

Recently, multi-energy systems (MESs), whereby different energy carriers are coupled together, have become popular. For a more efficient use of MESs, the optimal operation of these systems needs to be considered. This paper focuses on the day-ahead optimal schedule of an MES, including a combined heat and electricity (CHP) unit, a gas boiler, a PV system, and energy storage devices. Starting from a day-ahead PV point forecast, a non-parametric probabilistic forecast method is proposed to build the predicted interval and represent the uncertainty of PV generation. Afterwards, the MES is modeled as mixed-integer linear programming (MILP), and the scheduling problem is solved by interval optimization. To demonstrate the effectiveness of the proposed method, a case study is performed on a real industrial MES. The simulation results show that, by using only historical PV measurement data, the point forecaster reaches a normalized root-mean square error (NRMSE) of 14.24%, and the calibration of probabilistic forecast is improved by 10% compared to building distributions around point forecast. Moreover, the results of interval optimization show that the uncertainty of the PV system not only has an influence on the electrical part of the MES, but also causes a shift in the behavior of the thermal system.

Transmission system operators apply selective load-shedding plans to prevent system wide interruptions and black-outs due to generation and transmission adequacy issues. If the load-shedding plan is activated, the electricity supply is intentionally switched off in indicated areas for a fixed period of time. The burden of load-shedding plans thus falls on a subset of consumers, while the benefits accrue to all consumers. This results in public opposition as illustrated with the publication of the load-shedding plan in Belgium in the winter of 2014 - 2015. To improve the social acceptability of load-shedding plans, we analyzed the unfairness of load-shedding plans based on Gini-based inequality indices and studied a top-down socialized compensation scheme and bottom-up priority service contracts to indirectly reduce the unfairness. The analysis is executed for a simplified version of the Belgian load-shedding plan for the winter of 2014 - 2015.