Over the past few years, with more concerns on energy equity, energy security and environmental sustainability, peer-to-peer (P2P) energy trading market has increasingly attracted attention to energy users. With the rising electricity price and the growing public doubt to unsustainable development, more efforts are given to the integration between P2P energy trading and industrial practice. To date, the current research work has emphasized electricity cost reduction using demandside management (DSM) and P2P energy trading market to promote energy sharing and create a win–win situation for all industrial market participants. However, less attention has been given to evaluating industrial production. With this attention, this paper formulates a cost minimization problem for multiple discrete manufacturers, where DSM and P2P energy trading market are integrated to optimize manufacturing productivity and energy consumption. Moreover, this paper proposes a two-level reinforcement learning (RL) method to obtain the optimal DSM and energy trading strategies for discrete manufacturers. Unlike the existing RL approaches, the proposed method depicts a twolevel learning approach, where the upper-level program suggests a production plan for the manufacturer to improve the cost return, while the lower-level program optimizes the net benefits. Further, to verify the performance of the proposed method, simulations are conducted based on four different case studies, including different weather and load conditions. Numerical results demonstrate that the proposed algorithm provides a better production plan with enhanced learning speed and economic benefits compared to the existing RL approaches.

Nowadays, the restructuring of power systems is extremely urgent due to the depletion of fossil fuels on the one hand and the environmental impact on the other. In the restructured environment, the incorporation of renewable energy sources and storage devices is key as they have helped achieve a milestone in the form of microgrid technology. As the restructuring of the power system increases, there are several types of generation sources, and distribution companies express their interest in trading in a deregulated environment to operate economically. When considering the power system deregulation, the contract value deviates in some situations, resulting in an imbalance between the generation and the energy consumption, which can bring the system into a power outage condition. In particular, load frequency control has been a great challenge over the past few decades to ensure the stable operation of power systems. This study considers two generation sources: mini-hydro in GENCO-1 and 3 and microgrid (combination of wind, fuel cell, battery storage, and diesel engine) in GENCO-2 and 4. It is two equal-area networks; in area-1, GENCO-1 and 2, and in area-2, GENCO-3 and 4 are considered, respectively. In addition, a FOPID controller and two ancillary devices, such as a unified power flow controller and a superconducting magnetic energy storage system, have been incorporated. Three different test networks have been formed according to the contract value, such as unilateral, bilateral, and agreement violations. The simulation results show that ancillary devices and controller participation significantly enhance the system response by reducing the frequency and tie-line power fluctuation. To validate the efficacy of the proposed method, respective performance indices and percentages of improvement have been obtained. Finally, this study demonstrated the effectiveness of the proposed restructured power system in a deregulated environment.

In concern of energy trilemma such as energy security, energy equity, and environmental sustainability, the electric infrastructures are significantly developing the manifold avenues for decentralization, decarbonization, and digitalization. Over the years, the growth of decentralized power systems using distributed energy resources is increasing rapidly, bringing new prospects for local energy trading concerning economic and control operations in a single framework called transactive energy (TE). However, the continuity of power supply after high-disruptive events has remained a potential challenge for the energy market operator. Thus, to triumph over the challenges, this paper proposes a novel TE framework with resiliency consideration. On the other hand, cyber-attacks are an increasing concern, which impacts the digital platform, such as energy digitization. Considering this, homomorphic Blockchain technology is applied to secure the TE platform against cyber-attacks. To show the leveraging capabilities, this paper considers various case studies in the wake of high-disruptive events such as microgrid outage conditions and cyber-attack. The result shows that the proposed TE framework enhances the microgrid benefit and guarantees optimal trading performance. In addition, empowering Blockchain realizes that the security of the system is vital for the TE system when it faces data tampering or any external attacks. The effectiveness and feasibility of the proposed system are evaluated by cost, benefit, and probability of success.

Present work introduces a most popular evolution based algorithm and application of two degree of freedom proportional integral Derivative (TDOFPID) Controller based multi area power system. Differential Evolution (DE) optimization technique is applied here to tune the TDOFPID gains. Each area consists of Automatic generation control with addition of non-linarites. In this model time delay, Generation rate constraints (GRC) and reheat turbine are added to introduce non-linearity. At first attempt simulation is being done in two areas with DE optimization technique. Further a series connected Flexible Alternating Current Transmission (FACT) device such as interline power flow controller (IPFC) is included into the system and simulated. DE is also used to get the optimum value of TDOFPID controller having Integral time absolute error (ITAE) as the objective function. At last robustness analysis is done with varying parameter and different loading conditions. It is seen that, TDOFPID with IPFC gives better response compared to others.