Energies

Coordinated scheduling of distributed generation (DG), battery energy storage systems (BESS) and demand response (DR) is needed to reduce grid procurement costs without compromising customer comfort or network constraints. This paper proposes a two-stage framework for a grid-connected IEEE 33-bus microgrid. In the first stage, the Red Panda Optimization (RPO) algorithm jointly determines the locations and ratings of renewable DG units and BESS by minimizing power losses and voltage deviation. In the second stage, five customer agents with different cost–comfort preferences and three BESS agents learn their hourly schedules under real-time pricing using multi-agent Q-learning. Two incentive regimes are compared within the same decentralized framework: a price-only, customer-driven regime and an incentive-coordinated regime in which the grid operator broadcasts a shadow-price signal. Compared with the DG-only case, the two regimes reduced daily demand by 2.36% and 12.21%, energy loss by 4.53% and 27.79%, and procurement cost by 3.25% and 12.99%, respectively. Customer load curtailment provides all the demand and loss reduction and most of the cost reduction, while BESS arbitrage lowers the cost by a further 0.62 and 0.61% points at the expense of slightly higher losses. The incentive design, rather than the control architecture, largely determines how these benefits are shared between the operator and customers.

In 2024, the European Commission confirmed that the residential sector is responsible for 40% of total energy consumption in the EU and for 36% of greenhouse gas emissions, while 75% of buildings are still energy-inefficient. To cope with the climate crisis and to achieve the EU objectives of union climate neutrality, gas emission reduction and decarbonization, energy management systems of residential houses and energy optimization techniques are key aspects to investigate. In this context, this work focuses on the real-time energy optimization of energy communities whose customers cooperate to satisfy both the energy community’s and customers’ objectives. In particular, this work addresses the real-time energy management of an energy community composed of consumers and prosumers by proposing a cooperative multi-objective and multi-agent DQN control framework that takes into account the following: (a) the satisfaction of customers’ load demand, (b) the maximization of individual and collective self-consumption under fair energy sharing policies, (c) the management of the charging process of energy storage systems, (d) the efficient use of renewable energy sources, and (e) the guarantee of customer privacy. Simulation results have confirmed that an efficient scheduling of the available sources in a well-dimensioned energy community—in terms of load demand, renewable energy production and energy storage systems—meets the listed objectives, improves the flexibility of the energy community network, and reduces Grid usage.

The environmental consequences of digitalization remain contested. While digital technologies may support decarbonization through improved energy efficiency and resource allocation, empirical evidence also points to insignificant or adverse environmental associations. This study investigates whether the relationship between digitalization and carbon emissions varies with countries’ economic dependence on oil rents. Using a panel of 133 countries over 2000–2021, we construct a composite Digitalization Index through principal component analysis and estimate fixed-effects models with interactions across oil-rent dependence groups. The results indicate a negative but marginally significant association between digitalization and CO₂ emissions in economies with low oil-rent dependence. The interaction coefficients are positive for moderate- and high-dependence economies, indicating differences relative to the low-dependence reference group; however, the corresponding marginal associations are not statistically significant. The lagged specification yields a statistically significant negative association in low-dependence economies, while the marginal associations in the other groups remain nonsignificant. Several robustness specifications support heterogeneity in the estimated relationships, although the interaction effects lose statistical significance when country-specific linear trends are included. These findings suggest that the environmental association of economy-wide digital connectivity may depend on the economic importance of oil rents, but they do not establish a causal relationship. The study contributes to the digitalization–environment literature by identifying oil-rent dependence as a potential source of cross-country heterogeneity and highlights the importance of considering digital development alongside economic diversification and broader environmental policies.