To enhance the economic viability of integrated energy systems, it is important to balance risk and reward while ensuring operational flexibility and compliance with regulatory constraints. Developing an integrated risk measurement method for energy trading in energy markets that complies with regulatory constraints adds another layer of complexity, especially when dealing with large-scale heterogeneous energy systems, such as those dominated by green hydrogen. Ensuring adherence to market regulations, grid codes, and flexible requirements requires a thorough understanding of legal frameworks and policy considerations, as well as integrating regulatory constraints into the bidding/offering strategies. To address these challenges and enable the profitable development of integrated electricity-hydrogen energy systems (IEHSs), this paper develops a risk-constrained bidding/offering self-scheduling strategy for IEHSs that allows for simultaneous trading in day-ahead energy and reserve electricity markets. The developed self-scheduling strategy regulates hydrogen and power flow in a centralized manner, taking into account the operational limits of the power sector, hydrogen fueling stations (HFSs), electric vehicle parking lots, fuel cell vehicle parking lots, and renewable energy sources. As a salient feature, the important human-related factors in the mobility sector are included in the proposed strategy to motivate private owners to collaborate with IEHSs and to accommodate the charging demand of vehicles with electricity market signals. To obtain the robust day-ahead schedule, economic risks associated with renewable power generation, calls for reserve deployment, and electricity market prices are taken into consideration. Towards this end, a stochastic adaptive robust optimization approach is proposed to control the risk of profit variability, which is solved by a nested column-and-constraint generation algorithm. The effectiveness and feasibility of the proposed strategy are verified using the modified IEEE 14-bus test system, as a standard scale of local IEHSs. The numerical results highlight the synergies impact between HFSs and the power sector, resulting in an increase in the profit of the IEHS from $101.654k to $275.078k. Furthermore, the IEHS can improve its profit margin and competitiveness by employing the tri-level stochastic adaptive robust optimization approach. This approach ensures that the IEHS is resistant to sudden changes in real-time interactions, even with an 8.14% increase in the imbalance cost.@en

As an achievement of innovations resulting from partitioning mechanisms, these mechanisms can contribute to the more flexible operation of power systems in local communities. The ever-increasing frequency and severity of unexpected real-time failures have created challenges for partitioned-based power system operators, affecting each partition's resiliency. With this in mind, this paper presents an adaptive local operation strategy (ALOS) for resilient scheduling of the renewable-dominated partitioned-based power systems under normal and islanding modes in a decentralized manner. The main objective of the developed ALOS lies in reaching an affordable preparedness level in each partition to deal with unscheduled islanding mode, which can occur subsequent to real-time failures at common lines between adjacent partitions on transmission level. To this end, a set of resilience-target constraints is presented to prepare sufficient spinning reserve capacity in each partition to ensure continuity of supply during islanding mode. The proposed strategy is formulated as a two-stage stochastic mixed-integer linear program (MILP), and the nested formation algorithm is employed to execute it in a hierarchical fashion based on the privacy-preserving protocols. Besides, the tri-state compressed air energy storage (CAES) system is also included in the proposed strategy to mitigate the negative consequences caused by real-time failures and uncertain sources. Numerical results conducted on the IEEE 30-bus test system reveal that the proposed ALOS can enhance the resilience of each partition in responding to unscheduled islanding mode by efficiently utilizing all available capacities on the generation side. Furthermore, the DIgSILENT PowerFactory is used to identify the worst possible series of events and to evaluate the effectiveness of the proposed resilience-promoting proactive strategy in dealing with these events.

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This paper presents a new application of advanced SMPI controller for a newly introduced interconnected dynamic system with VSP based HVDC links for frequency control problem. This work presents an outgrowth of analysis about the Swarm – Based Optimization Algorithms (SBOAs) in the tuning process of Multivariable Proportional – Integral (MPI) controller, which is called swarm – based MPI (SMPI). PSO, GOA and GWO algorithms are used for tuning process of the designed SMPI. The VSP based HVDC model is added for mitigation of system frequency dynamics with emulating virtual inertia. The proposed SMPI controller are designed for enhancing the dynamic performance of this system's states during contingencies and they are compared with the conventional designed MPI controller. Deviation characteristics of the step function in MIMO transfer function of the VSP based HVDC model is considered as the common performance index in the proposed algorithms. On the other hand, the role of the proposed SMPI controller is to stabilize such interconnected system while minimizing the associated cost function. Matlab simulations next to the performed Nyquist's stability anslysis demonstrate how the tuned SMPI can remarkably improve the frequency deviations and the damping of the inter-area oscillations excited during a fault. This enhancement is more obvious especially when SMPI controller for a VSP based virtual inertia emulation is tuned using GWO method.

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This paper presents a comprehensive evaluation of the effect of quasi oppositional - based learning method utilization in output tracking control through a swarm-based multivariable Proportional-Integral-Derivative (SMPID) controller, which is tuned by a novel performance index based on the step response characteristics in multi-input multi-output (MIMO) system. The role of the proposed quasi oppositional based SMPID controller is to modify the tracking strategy on AC/HVDC interconnected systems while reducing the related cost function. The proposed analysis is established considering the most highly cited, well-known tested and newly expanded swarm-based optimization algorithms (SBOAs), such as Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO), Artificial Fish Swarm Algorithm (AFSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). These methods are used in the tuning process of multivariable PID (MPID) controller for output tracking control of an interconnected AC/DC system with virtual inertia emulation-based HVDC capabilities. The virtual inertia-based HVDC model, which is using a derivative technique, is attached for enhancing the system frequency dynamics with fast power injection during the contingency. The potential possibility for achieving a suitable assessment about the velocity reaction, the flexibility response, and the accuracy of the tracking process is provided by four different scenarios which are operated by step load changes as essential inputs in AC/HVDC interconnected MIMO system. Also the proposed fitness function, as deviation characteristics of the step response in MIMO transfer function in virtual inertia emulation based HVDC model, is compared with integral time absolute error (ITAE), as the standard performance index in the optimization process. The results are compared with the conventional tuned MPID (C - MPID) controller using MATLAB software. The obtained analysis emphasizes how the tuned SMPID can significantly increase the capability of tracking control on the proposed AC/HVDC interconnected model.

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This paper proposes a novel application for the optimal Linear Quadratic Gaussian (LQG) servo controller to enable a proper coordination of the AC/HVDC interconnected system with Virtual Synchronous Power (VSP) based inertia emulation. Particularly, the proposed control design takes the process disturbances and measurement noise of the studied VSP-HVDC system into account, while few studies have focused on this perspective. The proposed LQG controller with modifications is designed by means of a combination of Kalman Filter (state estimator) and an added Linear Quadratic Integrator (LQI) to observe the system model's states and track the reference commands while rejecting the effects of system noise. Besides, we utilize a swarm-based optimization algorithm to operate as the search process for the tuning of the elements in the weighting matrices involved in the controller design. The role of the proposed optimal LQG controller is to stabilize such AC/DC interconnected system with VSP-based inertia emulator while minimizing the associated performance index. According to the obtained simulation results, in addition to the advancement from the VSP-based approach for damping frequency oscillations excited by faults, application of the proposed LQG servo controller can achieve the targets on both estimating the state variables and tracking the reference signals with satisfactory performance, comparing with the conventional LQG regulator.

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Today, renewable resources have become one of the main pillars of electricity generation because of the constant reduction in their costs. In this regard, wind energy possesses the highest share in installed capacity and total energy output. Adopting a well-structured planning model is one of the most effective ways to further reduce costs and been the focus of research in the past decades. The previously models use a generic model for the wind turbine and its related parameters while only the optimal number is determined. In this context, this paper presents a novel approach for optimally planning of wind-diesel-battery systems which optimizes the wind turbine technology as a decision variable from the different types already commercially available. For this purpose, an optimization problem is introduced with the possibility of choosing a single type of turbine technology. The proposed model is then modified to optimize and simultaneously select multiple different wind turbine technologies. Results of the case study demonstrate a significant reduction in the planning cost, namely above 5 percent depend on the wind speed. Furthermore, the total yearly energy generated by diesel generator is decreased by 700 kWh, meaning higher renewable penetration and less emissions.

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A new application of Multivariable Proportional-Integral (MPI) controller with using evolutionary algorithms for a VSP based AC/DC interconnected power system model is proposed. The VSP based HVDC model is added for mitigation of system frequency dynamics by emulating virtual inertia. The designed heuristic-based multivariable PI controller is proposed for better performance of the system's states during contingencies. The proposed MPI (MPI) controller is modified by adding optimisation-based evolutionary algorithms to improve characteristic performance model versus conventional control methods. Simulations results demonstrate how the proposed MPI controller can optimally improve the performance of the power system, especially when a VSP base inertia emulation is activated in the system.@en

This paper proposes a new application for an optimal linear regulator for mitigation of frequency performance of an interconnected AC/DC system with (Virtual Synchronous Power) VSP based HVDC capabilities. The action of VSP, which is added into the HVDC control system, is to provide virtual inertia for the low-inertia system during a contingency. The proposed optimal regulator is designed to stabilize such ac/dc interconnected system while minimizing the associated cost function. For each of the presented controller, a different objective function is defined. This objective function is needed to tune the matrix gains and to process the optimum controller design. Simulations results demonstrate how the proposed regulator can significantly improve the reduction of frequency deviations and the damping of the interarea oscillations excited during a fault. This improvement is more obvious especially when a VSP-based virtual inertia emulation is activated in the system.

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Ever-increasing consumption of the electricity in the distribution networks encounters network planners with new challenges in terms of expansion requirements. Expanding substations is a technical concern considering various binding conditions. Emerging Battery Energy Storage Systems (BESSs) have the potential to defer substation expansion needs effectively. Although various applications of the BESSs are considered in the literature previously, application of the BESSs to defer substation expansion plans is not addressed adequately. In this context, this paper proposes a novel Multi-Objective Mixed Integer Linear Programming (MOMILP) for BESS operation in distribution networks to simultaneously substation expansion deferral and cost reduction. The proposed model is highly flexible with respect to the planner preferences, without convergence problems, and easily solvable using commercial solvers. The model determines charging and discharging scheduling of the active and reactive power of the BESSs optimally to achieve maximum substation expansion deferral without increasing operation costs. Higher degrees of the expansion deferral can be achieved at the expanse of the negligible cost increase. Furthermore, details of the whole BESS system including various parts and active/reactive power relations and limits are modeled accurately to better demonstrate real-life situations. Results of the simulations demonstrate accuracy and also functionality of the proposed method.

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The net-zero energy buildings are often supplied by renewable resources and energy storage systems. These energy resources have different seasonal and daily patterns of power production. Their output power is also uncertain. This paper aims to study these issues including daily-seasonal operation patterns, uncertainty, and cogeneration of various renewable resources and storage systems. These issues are investigated at net-zero energy building supported by renewable resources (i.e., solar energy, hydro energy, and fuelcell) and energy storage systems (i.e., hydrogen storage system). The uncertain parameters of the model are solar-hydro-load powers. The model minimizes the investment cost on solar system. The plan finds optimal sizing and operation for solar, hydro, hydrogen, and fuel-cell. The cooperation of hydrogen storage and fuelcell is optimized to level the uncertainty. The surplus of energy is fed into water electrolyzer to produce hydrogen and the fuelcell consumes the hydrogen to produce electricity. The seasonal operation is dealt by cogeneration of hydro-solar systems. The proposed plan installs 73 kW solar panel. The hydrogen storage system is charged at hours 7–17. When hydro power is increased to 39 kW, the building does not need the solar energy. The proposed model decreases the Carbon Dioxide by about 39546 kg. The model also reduces the total cost by about 50.3%.

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This paper addresses both the experimental and simulation studies on the application of overcurrent protective relay. The industrial overcurrent relays often have three individual settings. In this paper, the relay operating characteristics are set on normal inverse and very inverse curves. These curves operate as backup of each other. The third setting is also set on the instantaneous definite minimum time. The designated protection scheme protects the system with the least operating time.

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This paper optimizes the operation of electric vehicles in small charging stations deployed in electrical distribution networks. The grid is equipped with small-scale charging stations rather than one large-scale charging station. In addition, photovoltaic solar panels are installed on the grid to gain renewable energy benefits. The electric vehicles operate on vehicle-to-grid mode. The charging and discharging behavior of all the electric vehicles on all buses is optimized by the given method. The presented strategy optimizes the electric vehicles operation to damp out the renewable energy intermittency and energy cost reduction at the same time. And, it minimizes the charging-discharging cycles of the vehicles batteries in order to avoid battery degradation. The proposed problem is modeled as nonlinear stochastic programming including uncertainty of solar energy and solved by GAMS software. The results demonstrate that the suggested method can properly charge and discharge the vehicle-to-grid system. The vehicles are often charged on off-peak low-cost time periods and they are discharged at on-peak high-cost time-intervals. The Intermittency of solar cells is dealt with the achieved charging-discharging pattern for vehicle-to-grid system and cost of consumed energy is minimized by energy shifting. The results validate that the presented strategy can efficaciously achieve all the intended purposes at the same time.

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Renewable energies and electrical loads usually show short-term variations in their energy profiles and they need to be precisely modeled in terms of time-scale and uncertainty. Correlation of time-scale, uncertainty, and simulation time must be studied to make an optimal tradeoff between these parameters. This paper aims to deal with this issue and it studies the correlation of time-scale and uncertainty in the renewable energy simulation. The different time scales including 15, 30, and 60 min are modeled and simulated. Uncertainty of electrical loads and wind energy are also incorporated. The introduced model is simulated and investigated on a typical building for energy management. Energy management tool is simulated under multiple time-scale patterns and wind-load uncertainty. The model is expressed as mixed integer stochastic programming and results confirm that considering shorter time-scale results in more precise outputs. It is demonstrated that 30, 15, and 5-min time-scale reduce the cost about 5, 3, and 0.8%, respectively. But they increase the simulation time about 100, 200, and 300%, respectively. As a result, 15-min time-scale is considered as the best case because it keeps both simulation time and model accuracy on the acceptable level. It is also shown that uncertainty in model increases the cost about 22% and reduces load by 10% and decreases the cost about 38%.

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This paper utilizes the electric vehicles (EVs) connected to both the building and grid at the same time. In the building, the vehicle-to-home (V2H) operation is modeled. In the gird, the vehicle-to-grid (V2G) operation is studied. The EV s are modeled in the building connected to the electrical grid. The EV s are able to send energy to both grid and building. The building is also equipped with solar panels and electrical loads. Uncertainty of solar energy and loads are incorporated by stochastic programming. The optimization is implemented in GAMS to find the minimum daily cost. The optimal charging-discharging regime is denoted for EV s. The results illustrate that V2H - V2G operation can efficiently minimize the energy cost under solar-load volatility.

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This paper presents a hybrid model of an energy storage system including thermal and electrical energy storage systems in the building with thermal and electrical loads. Building receives its energy from electrical grid and purpose is to reduce the daily energy cost by optimal operation of hybrid energy storage system. Load forecast error is included as uncertainty and both thermal and electrical loads are modeled by Gaussian probability distribution function. The proposed problem for optimal cooperation of hybrid thermal-electrical storage systems is mathematically expressed as mixed integer binary linear programming. The scenario-based stochastic modelling is also included to deal with uncertainty in loading. The expressed stochastic optimization programming minimizes the daily energy cost in building and determines the optimal charging-discharging pattern for both thermal and electrical storage systems at the same time. The results demonstrate that electrical energy storage system reduces the cost about 15%, the thermal energy storage system decreases the cost about 17%, and coordinated thermal-electrical energy storage system reduces the cost about 34%. As a result, the best operation is achieved by the coordinated thermal-electrical energy storage system.

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