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This paper addresses the problem of economic/environmental optimal power flow with a multiobjective formulation using a second-order conic programming (SOCP) optimization model. This problem formulation considers renewable energy sources (RES), fossil-fuel-based power generation units, and voltage control. The proposed SOCP model is a stochastic scenario-based approach to deal with RES and load behavior uncertainties. An ε-constrained algorithm is used to handle the following three objective functions: (1) the costs of power generation, (2) active power losses in the branches, and (3) the emission of pollutant gases produced by fossil-fuel-based power generation units. For comparative purposes, the SOCP model is also presented using a linearized formulation, and numerical results are presented using a 118-bus system. The results confirm that changing the energy matrices directly affects the cost of objective functions. Additionally, using a linearized SOCP model significantly reduces reactive power violation in the generation units when compared to the nonlinearized SOCP model, but also increases the computational time consumed.

Battery storage is emerging as a key component of intelligent green electricitiy systems. The battery is monetized through market participation, which usually involves bidding. Bidding is a multi-objective optimization problem, involving targets such as maximizing market compensation and minimizing penalties for failing to provide the service and costs for battery aging. In this article, battery participation is investigated on primary frequency reserve markets. Reinforcement learning is applied for the optimization. In previous research, only simplified formulations of battery aging have been used in the reinforcement learning formulation, so it is unclear how the optimizer would perform with a real battery. In this article, a physics-based battery aging model is used to assess the aging. The contribution of this article is a methodology involving a realistic battery simulation to assess the performance of the trained RL agent with respect to battery aging in order to inform the selection of the weighting of the aging term in the RL reward formula. The RL agent performs day-ahead bidding on the Finnish Frequency Containment Reserves for Normal Operation market, with the objective of maximizing market compensation, minimizing market penalties and minimizing aging costs.

In prosumers’ communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large-scale use of home appliances in periods of lower solar radiation and low electricity tariff can impair the performance of the electrical system. The appearance of new consumption peaks can lead to disturbances. Moreover, the repetition of these events in the short term can cause rapid fatigue of the assets. To address these concerns, this research proposes a mixed-integer linear programming (MILP) model aiming at the optimal operation of the SBs and the appliance usage of each prosumer, as well as a PV plant within a community to achieve the maximum load factor (LF) increase. Constraints related to the household appliances, including the electric vehicle (EV), shared PV plant, and the SBs, are considered. Uncertainties in consumption habits are simulated using a Monte Carlo algorithm. The proposed model was solved using the CPLEX solver. The effectiveness of our proposed model is evaluated with/without the LF improvement. Results corroborate the efficient performance of the proposed tool. Financial benefits are obtained for both prosumers and the energy company.

Power distribution networks are transitioning from passive towards active networks considering the incorporation of distributed generation. Traditional energy networks require possible system upgrades due to the exponential growth of non-conventional energy resources. Thus, the cost concerns of the electric utilities regarding financial models of renewable energy sources (RES) call for the cost and benefit analysis of the networks prone to unprecedented RES integration. This paper provides an evaluation of photovoltaic (PV) hosting capacity (HC) subject to economical constraint by a probabilistic analysis based on Monte Carlo (MC) simulations to consider the stochastic nature of loads. The losses carry significance in terms of cost parameters, and this article focuses on HC investigation in terms of losses and their associated cost. The network losses followed a U-shaped trajectory with increasing PV penetration in the distribution network. In the investigated case networks, increased PV penetration reduced network costs up to around 40%, defined as a ratio to the feeding secondary transformer rating. Above 40%, the losses started to increase again and at 76–87% level, the network costs were the same as in the base cases of no PVs. This point was defined as the economical PV HC of the network. In the case of networks, this level of PV penetration did not yet lead to violations of network technical limits.

To achieve a successful integration of fluctuating renewable power generation, the power-to-heat (P2H) conversion is seen as an efficient solution that remedies the issue of curtailments as well as reduces carbon emissions prevailing in the district heating (DH) sector. Concurrently, the need for storage is also increasing to maintain a continuous power supply. Hence, this paper presents a MILP-based model to optimize the size of thermal storage required to satisfy the annual DH demand of a community solely by P2H conversion employing renewable energy. The DH is supplied by the optimal operation of a novel 2-km deep well heat pump system (DWHP) equipped with thermal storage. To avoid computational intractability, representative time steps with varying time duration are chosen by employing hierarchical agglomerative clustering that aggregates adjacent hours chronologically. The value of demand response and the effect of interannual weather variability are also analyzed. Numerical results from a Finnish case study show that P2H conversion utilizing small thermal storage in tandem with the DWHP is able to cover the annual DH demand, thus leading to a carbon-neutral DH system and, at the same time, mitigating the curtailment of excessive wind generation. Compared with the annual DH demand, an average thermal storage size of 29.17 MWh (2.58%) and 13.99 MWh (1.24%) are required in the business-as-usual and the demand response cases, respectively.

Preventing the medium voltage (MV) transformer fault by protecting transformers against indirect lightning strikes plays a crucial role in enhancing the continuous service to electricity consumers. Surge arresters, if selected properly, are efficient devices in providing adequate protection for MV transformers against transient overvoltage impulses while preventing unwanted service interruptions. However, compared to other protective devices such as the spark gap, their prices are relatively high. The higher the surge arrester rating and energy absorption capacity are, the higher the prices go. This paper proposes an inductor-based filter to limit the energy pushed into the surge arrester, and consequently to prevent any unwanted failure. An energy-controlled switch is proposed to simulate the fault of the surge arrester. Surge arresters with different ratings, e.g., 12 kV, 18 kV, 24 kV, 30 kV, 36 kV, and 42 kV with two different classes of energy, namely, type a and type b, are tested under different indirect lightning impulses such as 100 kV, 125 kV, 150 kV, 175 kV, 200 kV, 250 kV, 300 kV, and 500 kV. Furthermore, these surge arresters are equipped with different filter sizes of 100 μH, 250 μH, 500 μH, and 1 mH. Results prove that equipping a surge arrester with a proper filter size enhances the performance of the surge arrester significantly such that a high rating and somewhat expensive surge arrester can be replaced by a low rating and cheap surge arrester while providing similar or even better protective performance for MV transformers. Therefore, such configurations not only enhance the protective capability of surge arrester, but also reduce the planning and operating costs of MV networks.

Direct lightning strikes on overhead phase conductors result in high overvoltage stress on the medium voltage (MV) terminals of pole-mounted transformers, which may cause considerable damage. Therefore, introducing an efficient protection strategy would be a remedy for alleviating such undesirable damages. This paper investigates the optimized protection of MV transformers against direct lightning strikes on the phase conductors. To this end, first, the impacts of grounding densities (number of grounded intermediate poles between every two successive transformer poles) on the probability of overvoltage stress on transformer terminals are investigated. Then, the implications of guy wire, as a supporting device for ungrounded intermediate poles, on reducing the overvoltage stress on transformers, are studied. Finally, the role of a surge arrester in mitigating the overvoltage stress of non-surge-arrester-protected transformer poles is scrutinized. The investigations are conducted on a sample MV network with 82 wood poles comprising 17 pole-mounted transformers protected by spark gaps. To provide in-depth analysis, two different poles, namely creosote- and arsenic-impregnated poles, are considered under wet and dry weather conditions. A sensitivity analysis is performed on grounding distances and on a combination of guy wire and grounded intermediate poles while taking into account soil ionization. The results provide a clear picture for the system operator in deciding how many grounded intermediate poles might be required for a system to reach the desired probabilities of transformers experiencing overvoltage stress and how the surge arrester and guy wires contribute to mitigating undesirable overvoltage stress.

Protection of transformers, as one of the most expensive equipment in the power system, against lightning overvoltage impulses is a vital task. This paper, for the first time so far, investigates the effects of a filtered spark gap on the protection level of transformers against lightning overvoltage impulses. The filter is an inductor that is placed in series with the transformer and before the spark gap aiming to reduce the voltage at the connection point of the spark gap, and hence, enhancing the protection level of the transformer under lightning overvoltages. The experimental laboratory tests are accomplished on a 400 kVA, 22/0.4 kV, Delta-Star ( Δ − Y ) connection type transformer under 110 kV, and 125 kV overvoltage impulses, whereas the size of the spark gap is set to 80 mm and two inductors of 35 μ H and 119 μ H are considered. In order to perform a more in-depth analysis, a model that works reasonably close to the empirical case is developed in the EMTP-RV software. An optimization algorithm is used to determine the sensitive parameters of the double-exponential function, which is used to reproduce the applied laboratory lightning impulse voltages in the EMTP-RV environment. Moreover, the transformer is modeled according to the Cigre Guidelines (Working Group 02 of Study Committee 33). The behavior of the spark gap is simulated as close as the practical situation using the disruptive effect method. The preciseness of the simulated filtered spark gap model is verified by comparing the results of the simulated model in the EMTP-RV with the results of experimental tests. After verifying the model, different sizes of inductors are studied in the EMTP-RV environment to investigate whether larger or smaller inductors provide better protection for the transformer under lightning conditions. A comparison is performed among the conventional spark gap, surge arrester, and the filtered spark gap to provide a better analysis of the potential of the proposed device. The results indicate that proper sizing of the inductor, within an effective range, slightly enhances the protection level of the transformer.

Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.

Efficient utilization of renewable generation inside microgrids remains challenging. In most existing studies, the goal is to optimize the energy cost of microgrids by working in synergy with the main grid. This work aimed at maximizing the self-consumption of on-site photovoltaic (PV) generation using an electrical storage, as well as demand response solutions, in a building that was also capable of interacting with the main grid. Ten-minute resolution data were used to capture the temporal behavior of the weather. Extensive mathematical models were employed to estimate the demand for hot-water consumption, space cooling, and heating loads. The proposed framework is cast as mixed-integer linear programming model while minimizing the interaction with the grid. To evaluate the effectiveness of the proposed framework, it was applied to a typical Finnish household. Matching indices were used to evaluate the degree of overlap between generation and demand under different PV penetrations and storage capacities. Despite negative correlation of PV generation with Finnish seasonal consumption, a significant portion of demand can be satisfied solely with on-site PV generation during the spring and summer seasons.