Value of integrated electricity and heat scheduling with considering TSO–DSO cooperation

The active role of distribution system operators (DSOs) coordinated with the transmission system operator (TSO) is highlighted by increasing the competition of new parties in energy markets. Besides, combined heat and power (CHP) units are the primary heat supplier in district heating systems, and their electricity production is strongly coupled with heat productions. This paper evaluates an integrated model for scheduling electricity and heat with considering TSO–DSO cooperation to increase the operating efficiency in the day-ahead horizon. Also, this paper considers the cooperation of the electrical TSO, electrical DSOs, and district heating systems’ operators. The proposed model facilitates energy transactions between systems by considering intermediary variables. The share of each market party is calculated using intermediary variables and locational marginal prices of electrical and heating systems, and the values are compared to the cases with the isolated operating of energy systems. Also, the model considers thermal energy storage systems (TESs) and the heat transaction capability between neighbor systems, and the feasible convex region is used for the operation of CHP units. The DC power flow equations are used at the transmission level, while the AC power flow is used for distribution grids. The AC power flow equations are relaxed into the second-order cone programming (SOCP) formulation, which results in a mixed-integer second-order cone programming (MISOCP) problem. The proposed model is applied to the modified IEEE 24-bus test system, which contains electrical and heating systems at the distribution level. The result shows that the proposed model successfully reduces the operational costs and energy prices compared to the isolated scheduling of energy systems. Also, the model facilitates energy trading between market parties

A privacy-preserving approach to day-ahead TSO-DSO coordinated stochastic scheduling for energy and reserve

Proliferation of distributed energy resources (DERs) calls for a coordinated transmission and distribution (T&D) scheduling at the day-ahead stage. The problem becomes more complicated dealing with the variability of stochastic parameters. Also, privacy and complexity are two barriers to the development of such coordinated platforms. This paper addresses these issues by introducing a hybrid centrally-supported decentralized stochastic framework for the day-ahead energy and reserve market with minimum complexity and the need for data-sharing between system operators. The proposed model is able to calculate the bidirectional power exchange at the T&D interface and the separated costs, dispatches, and reserves of all market participants. The proposed model does not consider any priority for operators and increases the liquidity by facilitating participants’ access to the market platform. Also, the second-order cone programming (SOCP) formulation is used for calculating the AC power flow of distribution grids, and the model is validated and compared with other implementation strategies. The proposed model is implemented on a modified IEEE 24-bus test system, and results show that the model can schedule resources for supplying energy and reserves in both transmission and distribution levels in an acceptable computation time

Stochastic Procurement of Fast Reserve Services in Renewable Integrated Power Systems

Ensuring the security and quality of supply in a power system after a contingency event is one of the most challenging tasks for an electricity system operator. This work is initiated by this challenge and proposes a solution based on the use of provided reserves by fast generators, storage devices, and wind farms. A coordinated model is proposed in a joint energy and reserves market considering their corresponding cost to ensure the adequacy in the simultaneous deployment of reserves for the different sources of uncertainties. The Benders decomposition approach is used in the modeling of the stochastic security-constrained unit commitment, and considering the large-scale and complex nature of the model, acceleration techniques are suggested to reduce the execution time. The proposed model is tested on the 6-bus and the IEEE 118-bus test systems. Numerical results show that the optimal values of reserves successfully address contingencies in both of the critical and normal periods after the contingencies and the optimal solution is calculated in a reasonable computing time

Probabilistic Planning of Virtually-Hybrid Harmonic Filters in Modern Distribution Systems

Peer reviewe

PV hosting capacity improvement through an aggregate study of single-tuned passive filter planning and grid reconfiguration

The high penetration of Photovoltaic (PV) units may lead to harmonic and overvoltage problems in electrical distribution systems. Accordingly, the PV Hosting Capacity (PVHC) of the grid is limited when the distribution network faces high penetration of harmonic-injecting loads or low demand-level conditions. On the other hand, the combination of passive filters and grid reconfiguration can improve the voltage profile and decrease the distortion levels in the distribution systems. Hence, in this study, an aggregate optimization of passive harmonic filters and grid topology is implemented to maximize the PVHC of the active distribution systems. Accordingly, the planning study is applied to provide the possibility of maximum PV installation capacity in the network while the grid indexes are maintained within the standard limits. It should be noted that the study is constrained by the total filter number and maximum allowed grid power loss. According to the simulation results of a sample grid without the capability of PV installation in the base case, the PVHC can be improved to 78% using the proposed strategy.acceptedVersionPeer reviewe

SoS-based multiobjective distribution system expansion planning

This paper coordinates the reconfiguration of distribution systems with the expansion problem while the potential of demand response (DR) programs and distributed generation (DG) units are modeled in the active distribution expansion planning. The concept of system of systems (SoS) is proposed to model the expansion of DGs that are owned by private investors. SoS is an efficient system consisting of some autonomous and heterogeneous systems with distinct objective functions. According to the concept of SoS, a decision-making paradigm is developed to determine the location, size, and time of DG investment made by a commercial agent, as well as the price of generated power. From the distribution company (DISCO) viewpoint, the proposed model is a multi-objective (MO) optimization problem. The first objective function is the net present value of the total investment and operation costs related to the network. The second objective function is a reliability index, i.e. the expected energy not-supplied (EENS). The uncertainty of load growth in future years is handled by using a scenario-based approach. The introduced problem is solved by using multi-objective particle swarm optimization (MOPSO) algorithm empowered with an innovative three-layer procedure that is provided to better manage the space of the decision variables. The first layer is based on PSO particles while the second and third layers are based on a sensitivity analysis. Finally, a standard 33-bus distribution system is utilized to obtain the simulation results that show the performance and advantages of the proposed method

Stochastic System of Systems Architecture for Adaptive Expansion of Smart Distribution Grids

The incorporation of the reconfiguration into the expansion planning of smart distribution networks is addressed in this paper, in which the potential of distributed energy resources and demand response (DR) are modeled. The system of systems (SoS) architecture is employed to model the strategy of a distribution company (DISCO), a private investor (PI), and a DR provider (DRP). The SoS is an efficient modeling architecture to model the behavior of independent and autonomous systems with distinct objective functions who are able to share some data and work together. The aim of the DISCO is to upgrade the system with the optimal cost and reliability, whereas the PI and DRP want to maximize their profit. The DISCO should try to persuade the PI to install DGs (Distributed generations) by offering the guaranteed purchasing prices. Furthermore, the DRP is a market player who can negotiate with the DISCO to sign a contract to sell the purchased DR capacities from the customers. The uncertainties of the DISCO problem is handled by using the chance-constraint method, but the PI and DRP use the conditional value at risk method to model their uncertainties. Finally, to solve the proposed model, the multiobjective optimization algorithm is employed

Hosting capacity enhancement and voltage profile improvement using series power electronic compensator in LV distribution networks

Different types of custom power devices proposed by the researchers can provide a diverse set of services in electrical energy systems. Among them, Series Power Electronic Compensators (SPEC) as Dynamic Voltage Conditioner (DVC) which are connected to the substation of the LV networks can effectively improve the voltage characteristics of the grid in a simple and practical way. In this paper, firstly a short review is given on the effect of the SPEC on the voltage profile of a LV distribution system with real measured data. Moreover, the grid hosting capacity improvement using SPEC is addressed and studied in the LV grid. Hence, a supplementary evaluation is performed by a focus on the min-max voltage curves with respect to the different demand-leveling scenarios. According to the results, the use of a SPEC can effectively enhance the network hosting capacity beside the considerable improvements in the grid voltage. Therefore, it can be effectively applied in modern networks to provide higher levels of power quality in presence of high load or generation conditions. So doing, it should be possible to increase the hosting capacity of a network and therefore to increase the amount of loads and distributed energy resources that the electric distribution network can reliably accommodate without significant grid upgrades.acceptedVersionPeer reviewe

An efficient linear model for optimal day ahead scheduling of CHP units in active distribution networks considering load commitment programs

The Optimal day-ahead Scheduling of Combined Heat and Power (OSCHP) units is a crucial problem in the energy management of Active Distribution Networks (ADNs), especially in the presence of Electrical and Thermal Energy Storages considering Load Commitment (LC) programs. The ADN operator may use Combined Heat and Power (CHP) units to supply its Industrial Customers (ICs) and can transact electricity with the upstream wholesale electricity market. The OSCHP problem is a Mixed Integer Non Linear Programming (MINLP) problem with many variables and constraints. However, the optimal operation of CHP units, Electrical and Thermal Energy Storages considering LC programs and contingency scenarios, may highly complicate this problem. In this paper, linearization techniques are adopted to linearize equations and a two-stage Stochastic Mixed-Integer Linear Programming (SMILP) model is utilized to solve the problem. The first stage models the behavior of operation parameters and minimizes the operation costs, verifies the feasibility of the ICs' requested power exchanges and the second stage considers LC programs and the system's stochastic contingency scenarios. The effectiveness of the proposed algorithm has been demonstrated considering 18-bus, 33-bus and 123-bus IEEE test systems