The optimal and sustainable management of water distribution systems still represent an arduous task. In many instances, especially in aging water net-works, pressure management is imperative for reducing breakages and leakages. Therefore, optimal District Metered Areas represent an effective solution to decreasing the overall energy input without performance compromise. Within this context, this paper proposes a novel adaptive management framework for water distribution systems by reconfiguring the original network layout into (dynamic) district metered areas. It utilises a multiscale clustering algorithm to schedule district aggregation/desegregation, whilst delivering energy and supply management goals. The resulting framework was tested in a water utility network for the simultaneously production of energy during the day (by means of the installation of micro-hydropower systems) and for the reduction of water leakage during the night. From computational viewpoint, this was found to significantly reduce the time and complexity during the clustering and the dividing phase. In addition, in this case, a recovered energy potential of 19 MWh per year and leakage reduction of up to 16% was found. The addition of pump-as-turbines was also found to reduce investment and maintenance costs, giving improved reliability to the monitoring stations. The financial analyses to define the optimal period in which to invest also showed the economic feasibility of the proposed solution, which assures, in the analysed case study, a positive annual net income in just five years. This study demonstrates that the combined optimisation, energy recovery and creation of optimized multiple-task district stations lead to an efficient, resilient, sustainable, and low-cost management strategy for water distribution networks.
Keywords: Water distribution systems | Micro-hydropower systems | Sustainable and smart cities | Water-energy nexus | Water leakage reduction | Financial return-on-investment

Wind power energy has been showing significant growth in installed capacity around the world. This opportunity presents big challenges to operate power systems with high wind power penetration levels, considering the variability and intermittent behavior of this type of power source. To reduce uncertainties associated with this kind of power systems, researchers have explored the integration of wind power energy with other renewable energy sources, like solar and hydropower. For instance, the integration of wind and hydro systems can deal with the spatial and temporal complementarity of hydrological and wind regimes to produce energy. Therefore, it is necessary to consider the stochastic behavior and the dependence structures between these variables to define better operational policies. This study explores the spatial correlation of hydrological and wind regimes in different regions of Brazil and defines an entropy-copula-based model for the joint simulation of monthly streamflow and wind speed time series to evaluate the potential integration of hydro and wind energy sources. The proposed model showed a good adherence to the periodic behavior for both variables, and the results indicate that simulated scenarios preserved statistical features of historical data
Keywords: Hydro-wind complementary | Renewable energy | Stochastic modeling

High performance and cost-effective ferry boats are of capital interest for customers and marine industry companies. On the other hand, the traditional ferry boats, operated by diesel generators, spatter the atmosphere with CO2 emissions and detrimental particles. Hence, electric propulsion in marine applications, especially in ferry vessel systems, has gained a lot of attention during the last decade as a promising technology to decrease fuel consumption and emissions. However, one of the main issues in the electric ferries (E-Ferry) is to keep the voltage and frequency within an acceptable range according to the large dynamic load fluctuations. In order to solve this issue, this paper presents a model predictive energy management based on a modified black hole algorithm (BHA) for the hybrid E-Ferry systems. Finally, to study the efficiency of our proposal, we run a real-time simulation using the d-Space simulator and compare the effect of the prediction horizon on the system performance.
Keywords: Marine power system | Model predictive control (MPC) | Electric ferry-system | Fuel cell technology | Black hole algorithm (BHA)

To learn human intelligence, the social system/human system is added to a cyber–physical energy system in this paper. To accelerate the configuration process of the parameters of the cyber–physical energy system, parallel systems based on artificial societies-computational experiments-parallel execution are added to the cyber–physical energy system, i.e., a parallel cyber–physical–social energy system is proposed in this paper. This paper proposes a real-time generation control framework to replace the conventional generation control framework with multiple time scales, which consist of long-term time scale, short-term time scale, and real-time scale. Since a lazy operator employed into reinforcement learning, a lazy reinforcement learning is proposed for the real-time generation control framework. To reduce the real simulation time, multiple virtual parallel cyber–physical–social energy systems and a real parallel cyber–physical–social energy system are built for the real-time generation control of large-scale multi-area interconnected power systems. Compared with a total of 146016 conventional generation control algorithms and a relaxed artificial neural network in the simulation of IEEE 10-generator 39-bus New-England power system, the proposed lazy reinforcement learning based realtime generation control controller can obtain the highest control performance. The active power between two areas and the systemic frequency deviation can be reduced by the lazy reinforcement learning, and the simulation results verify the effectiveness and feasibility of the proposed lazy reinforcement learning based real-time generation control controller for the parallel cyber–physical–social energy systems.
Keywords: Lazy reinforcement learning | Real-time generation control | Parallel cyber–physical–social energy systems | Artificial societies-computational | experiments-parallel execution | Unified time scale

The world is experiencing a fourth industrial revolution. Rapid development of technologies is advancing smart infrastructure opportunities. Experts observe decarbonisation, digitalisation and decentralisation as the main drivers for change. In electrical power systems a downturn of centralised conventional fossil fuel fired power plants and increased proportion of distributed power generation adds to the already troublesome outlook for op- erators of low-inertia energy systems. In the absence of reliable real-time demand forecasting measures, effective decentralised demand-side energy planning is often problematic. In this work we formulate a simple yet highly effective lumped model for forecasting the rate at which electricity is consumed. The methodology presented focuses on the potential adoption by a regional electricity network operator with inadequate real-time energy data who requires knowledge of the wider aggregated future rate of energy consumption. Thus, contributing to a reduction in the demand of state-owned generation power plants. The forecasting session is constructed initially through analysis of a chronological sequence of discrete observations. Historical demand data shows behaviour that allows the use of dimensionality reduction techniques. Combined with piecewise interpolation an electricity demand forecasting methodology is formulated. Solutions of short-term forecasting problems provide credible predictions for energy demand. Calculations for medium-term forecasts that extend beyond 6-months are also very promising. The forecasting method provides a way to advance a novel decentralised informatics, optimisa- tion and control framework for small island power systems or distributed grid-edge systems as part of an evolving demand response service.
Keywords: Demand response | Decentralised | Grid edge | Time series forecasting