Monopolistic and Game-Based Approaches to Transact Energy Flexibility

Coordinated deliverable energy flexibility and regulation capacity of distribution networks

Coordinated operation of pumped-storage hydropower with power and water distribution systems

Coordinated restoration optimization of power-gas integrated energy system with mobile emergency sources

As the global demand for freshwater resources continues to grow at a fast pace, energy costs associated with treating and distributing water in urban areas are projected to increase. A promising solution to enhance the efficiency and reduce these costs is to coordinate the operation of water systems with power distribution systems operation. In this paper, we propose an optimization framework which integrates the energy flexibility of water treatment, desalination and distribution systems in power distribution systems operation. In the proposed framework, a water distribution system operator (W-DSO) co-optimizes the operation of variable speed pumps and water storage tanks in water treatment, desalination and distribution systems for minimizing its daily energy costs, given the expected water demand and energy tariffs. The optimized energy flexibility of W - DSO is then incorporated in the operation of power distribution systems. The proposed framework takes into account the operational constraints of both power and water distribution networks, thus co-optimizing their operation without endangering the reliable supply of power and water to customers. Simulation results demonstrate the merits of the proposed model in enhancing the economic efficiency of the test 15-node water network supplied by a 33-bus power distribution system.

With the rising electricity demand for freshwater production, power and water sectors are becoming increasingly interdependent. This vital link could create new opportunities for power and water distribution system operators to coordinate the operation of their systems and achieve operational cost and energy efficiency benefits. This paper proposes the flexible power-water flow (FlexPWF) model for co-optimizing the operation of flexible energy-intensive components in water systems with the operation of both power and water distribution systems. The FlexPWF model leverages the energy flexibility provided by water treatment and desalination plants in conjunction with variable speed pumps and water storage tanks to minimize the operating costs of interdependent power and water distribution systems. The proposed model takes into account the operational constraints of both power and water distribution systems, thus coordinating their operation without endangering the reliable supply of power and water to customers. The paper highlights the merits of integrating water treatment and desalination plants, where a detailed model is proposed to capture their dynamic operation for producing variable amounts of freshwater and providing energy flexibility. Simulations, conducted on the 33-bus test distribution system and a 15-node test water distribution system, show that the FlexPWF model effectively coordinates the operation, reduces the operating costs and improves the operational metrics of both power and water systems.

Increasing penetration of distributed energy resources in power distribution systems and appearing the flexible behavior of end-users based on demand response programs make the distribution layer of the power systems more active. In this way, energy transaction management through a decentralized manner could be an appropriate solution to improve the efficiency of energy trading in the distribution power networks. This paper proposes a decentralized method to manage energy flexibility by consumers based on a bottom-up approach in distributed power systems. Also, a 33-bus distribution network is considered to assess the performance of our proposed decentralized energy flexibility management model based on impacts of flexible behaviors of end-user and uncertainty of distribution lines to flow energy in the power network.

The paradigm shift in adopting electrified public transportation, enabled by long-range battery electric buses (BEBs) and associated charging infrastructure, inflict operational challenges to both power distribution and transit systems. This article takes an opportunistic look at the paradigm shift and develops a model for optimally scheduling the spatio-temporal charging flexibility of BEBs as a source of operational flexibility for power distribution systems. The proposed model co-optimizes the timing and location of BEBs' charging in the transit system with the operation of the power distribution system, while respecting the operational constraints and requirements of both power distribution and public transit systems. In the proposed model, the BEBs transit system is mathematically modeled by a graph and the associated BEB charging stations are mapped into the power distribution nodes. The BEBs' transit schedule constrains the spatio-temporal charging opportunities to all BEBs over the scheduling horizon. The proposed model is implemented on the IEEE 33-bus power distribution system coupled with the transit system of Park City, UT, USA, with BEB routes. The numerical results showcase the economic and operational benefits of the proposed model for the power distribution system operator and transit system operator.

This paper proposes a fundamental model for defining and optimizing distributed energy flexibility in distribution buses, as well as deliverable energy flexibility as the aggregate distributed flexibility that is available for offering to the day-ahead energy market by distribution system operators (DSOs), without jeopardizing the operational constraints of the distribution network. The distributed energy flexibility is provided by flexible loads in distribution buses, which are modeled by clustered queuing systems representing the aggregation of large population of flexible loads with different energy and service quality requirements. Further, controllable inverters interfacing distributed solar generation provide reactive power flexibility in distribution buses. The deliverable energy flexibility is optimized by the proposed model that coordinates the energy flexibility of queued flexible loads and controllable inverters to maximize DSO's profit of participating in the day-ahead energy market, while satisfying the service quality constraints of flexible loads, the operational limits of controllable inverters, and power flow constraints of distribution network that are formulated using the branch flow model. Moreover, an index is proposed to calculate the contribution of each distribution bus in providing the deliverable energy flexibility. The proposed model is implemented on the IEEE 33-bus distribution network. The numerical results exhibit profit opportunities for DSOs from providing the deliverable energy flexibility in the day-ahead energy market.

In distribution systems, load demand can exceed the feeder thermal limit during peak demand periods and cause system damage. This situation can be prevented by load shedding, which results in financial losses to utilities and customers. The problem can be countered by utilizing energy storage systems (ESS) properly. In this paper, we formulate an optimization problem combining the utilization of ESS and wind power in a typical distribution system, whereby real power is optimally scheduled in an electricity market under the constraint that the load demand cannot exceed feeder thermal limit. This approach ensures the reliable operation of the power distribution system and prevents outages. The developed optimization problem is solved for a typical distribution system. The results clearly demonstrate the economical operation of ESS with wind power in distribution systems to prevent outages and maximize the ESS profit.

This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This paper also takes into account the local and the coupling/infrastructure constraints of EVs, transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices.

With the development of the economy, electricity demand continues to increase, and the time for electricity consumption is concentrated, which leads to increasing pressure on the voltage regulation of the distribution network. For example, a large number of electric vehicles charging during a low-price period may cause the problem of under-voltage of the distribution network. On the other hand, the penetration of distributed power generation of renewable energy may cause over-voltage problems in the distribution network. This study proposes a Stackelberg game model between the distribution system operator and the load aggregator. In the Stackelberg game model, the distribution system operator affects the users’ electricity consumption time by issuing subsidies to decrease the frequency of voltage violations. As the representative of users, the load aggregator helps the users schedule the demand during the subsidized period to maximize profits. Case studies are carried out on the IEEE 33-bus power distribution system. The results show that the time of the subsidy can be optimized based on the Stackelberg game model. Both the distribution system operator and the load aggregator can obtain the optimal economic profits and then comprehensively improve the operating reliability and economy of the power distribution system.

The efficient planning and operation of power distribution systems are becoming increasingly significant with the integration of renewable energy options into power distribution networks. Keeping voltage magnitudes within permissible ranges is vital; hence, control devices, such as tap changers, voltage regulators, and capacitors, are used in power distribution systems. This study presents an optimization model that is based on three heuristic approaches, namely, particle swarm optimization, imperialist competitive algorithm, and moth flame optimization, for solving the voltage deviation problem. Two different load profiles are used to test the three modified algorithms on IEEE 123- and IEEE 13-bus test systems. The proposed optimization model uses three different cases: Case 1, changing the tap positions of the regulators; Case 2, changing the capacitor sizes; and Case 3, integrating Cases 1 and 2 and changing the locations of the capacitors. The numerical results of the optimization model using the three heuristic algorithms are given for the two specified load profiles.

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Multiscale design for system-wide peer-to-peer energy trading

Traditionally, the distribution system operator (DSO) is responsible for the reliable operation of power distribution systems. However, the advent of micro-grids in electric power distribution systems promotes a new role of DSO where it is responsible for aggregating the widely dispersed distributed energy resources (DERs), small thermal generation units, and flexible loads into the electricity markets. This paper presents an optimal demand response (DR) bidding framework for aggregators in the distribution network, that help integrate the uncertainty of the power output of the wind turbine. In the proposed framework, the load aggregators collect and submit DR offers to the DSO to make their contribution to the market operation. The load reduction offers include load curtailment, load shifting, and the generation from DERs. The DSO solves market clearing problem using the proposed DR model for the day-ahead market using a mixed-integer linear programming (MILP) model. The proposed approach for DR participation and market clearing is implemented using a 6-bus test system, and the merits of the proposed DR model are demonstrated using two cases for hourly unit commitment and ten scenarios for wind variability.