Abstract
There is a growing need for increased flexibility in modern power systems. Traditionally, this flexibility has been provided by supply-side technologies. There has been an increase in the research surrounding flexibility services provided by demand-side actors and technologies, especially flexibility services provided by prosumers (those customers who both produce and consume electricity). This work gathers 1183 peer-reviewed journal articles concerning the topic and uses them to identify the current state of the art. This body of literature was analysed with two leading textual and scientometric analysis tools, SAS (c) Visual Text Analytics and VOSviewer, in order to provide a detailed understanding of the current state-of-the-art research on prosumer flexibility. Trends, key ideas, opportunities and challenges were identified and discussed.

Abstract
The integrated use of electricity and natural gas has captured great attention over recent years, mainly due to the high efficiency and economic considerations. According to the energy hub design and operation, which allows using different energy carriers, it has turned into a critical topic. This paper develops a two-stage stochastic model for energy hub planning and operation. The uncertainties of the problem have arisen from the electric, heating, and cooling load demand forecasts, besides the intermittent output of the solar photovoltaic (PV) system. The scenarios of the uncertain parameters are generated using the Monte-Carlo simulation (MCS), and the backward scenario reduction technique is used to alleviate the number of generated scenarios. Furthermore, this paper investigates the effectiveness of demand response programs (DRPs). The presented model includes two stages, where at the first stage, the optimal energy hub design is carried out utilizing the particle swarm optimization (PSO) algorithm. In this respect, the capacity of the candidate assets has been considered continuous, enabling the planning entity to precisely design the energy hub. The problem of the optimal energy hub operation is introduced at the second stage of the model formulated as mixed-integer non-linear programming (MINLP). The proposed framework is simulated using a typical energy hub to verify its effectiveness and efficiency.

Abstract
A highly versatile optimal task scheduling algorithm is proposed, inspired by Dijkstra's shortest path algorithm. The proposed algorithm is named "Dijkstra Optimal Tasking" (DOT) and is implemented in a generic manner allowing it to be applicable on a plethora of tasking problems In this study, the application of the proposed DOT algorithm is demonstrated for industrial setting in which a set of tasks must be performed by a mobile agent transiting between charging stations. The DOT algorithm is demonstrated by determining the optimal task schedule for the mobile agent which maximizes the speed of task achievement while minimizing the movement, and thereby energy consumption, cost. A discussion into the anticipated plethora of applications of this algorithm in different sectors is examined.

Abstract
The integration of new technologies at the residential level such as energy storage systems, electric vehicles, solar photovoltaic generation and mini wind turbines triggered the appearance of a new agent in the power systems called prosumers. This agent has the potential to provide new forms of flexibility and cost-effective solutions. However, associated with these new solutions there are also a number of problems that affect these solutions, particularly network constraints. This work presents an analysis not only on the benefits of utilizing the prosumer's flexibility but also to the problems associated with the operation and optimization of the network. A new model is presented that considers energy transactions between prosumers in the neighborhood and between them and the network using on a stochastic framework, in order to account for a set of uncertainties in the form of scenarios associated with the availability of various resources and technologies. The results show the economic benefit of energy transactions between prosumers resulting in more flexibility for the system while highlighting the effect of network restrictions and potential problems associated with them.

Abstract
Integrated transmission expansion planning (TEP) and generation expansion planning (GEP) with Wind Farms (WFs) is addressed in this paper. The optimal number of expanded lines, the optimal capacity of WFs installed capacity, and the optimal capacity of wind farms lines (WFLs) are determined through a new TEP optimization model. Furthermore, the optimum capacity additions including conventional generating units is obtained in the proposed model. The Benders decomposition approach is used for solving the optimization problem, including a master problem and two sub-problems with internal scenario analysis. In order to reduce the computational burden of the multi-year and multi-objective expansion planning problem, a multi-stage framework is presented in this paper. The uncertainties of wind speed and system demand along with contingency scenarios lead to a probabilistic optimization problem. Moreover, in the proposed model, the planning time horizon is divided into three predefined stages. This multi-stage approach is used to increase the proposed model accuracy in a power system with a high level of wind power penetration. Hence, in this paper a scenario-based probabilistic multistage model for transmission expansion planning is proposed, incorporating optimal WFs integration. It is recognized that high wind penetration increases the transmission expansion investment cost, but based on the reduction of the investment cost of conventional units, the total system cost will be smaller. This result emphasizes the main advantage of wind generating system over the conventional generating system. This planning methodology is applied to the modified IEEE 24-bus test system and simplified Iran 400-kV real system to show the feasibility of the proposed algorithm.

Abstract
There is an ongoing paradigm shift occurring in the electricity sector. In particular, previously passive consumers are now becoming active prosumers and they can now offer important and cost-effective new forms of flexibility and demand response potential to the electricity sector and this can translate into system-wide operational and economic benefits. This work focuses on developing a model where prosumers participate in demand response programs through varying tariff schemes, and the model also quantifies the benefits of this flexibility and cost-reductions. This work includes transactive energy trading between various prosumers, the grid and the neighborhood. A stochastic tool is developed for this analysis, which also allows the quantification of the collective behavior so that the periods with the greatest demand response potential can be identified. Numerical results indicate that the optimization of energy transactions amongst the prosumers, and including the grid, leads to considerable cost reductions as well as introducing additional flexibility in the presence of demand response mechanisms.