Distribution Locational Marginal Prices Research Articles

DLMP using three‐phase current injection OPF with renewables and demand response

With increasing penetration of demand response and distributed renewable generation in the distribution system, distribution locational marginal price (DLMP) helps to provide a right price signal for participants in an active distribution system. Unlike the transmission system, the distribution system is unbalanced, which may result in different phase price at each bus. In this study, a three-phase current injection based optimal power flow (OPF) with robust convergence is proposed to compute DLMP at each bus and phase. Several scenarios have been modelled to validate the application of the proposed DLMP to manage distributed generation, demand response and line congestion for a modified IEEE test system.

Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City

The use of electric vehicles (EVs) is growing in popularity each year, and as a result, considerable demand increase is expected in the distribution network (DN). Additionally, the uncertainty of EV user behavior is high, making it urgent to understand its impact on the network. Thus, this paper proposes an EV user behavior simulator, which operates in conjunction with an innovative smart distribution locational marginal pricing based on operation/reconfiguration, for the purpose of understanding the impact of the dynamic energy pricing on both sides: the grid and the user. The main goal, besides the distribution system operator (DSO) expenditure minimization, is to understand how and to what extent dynamic pricing of energy for EV charging can positively affect the operation of the smart grid and the EV charging cost. A smart city with a 13-bus DN and a high penetration of distributed energy resources is used to demonstrate the application of the proposed models. The results demonstrate that dynamic energy pricing for EV charging is an efficient approach that increases monetary savings considerably for both the DSO and EV users.

Application of Distribution Locational Marginal Price in optimal simultaneous distributed generation placement and sizing in electricity distribution networks

Application of Distribution Locational Marginal Price in optimal simultaneous distributed generation placement and sizing in electricity distribution networks

Congestion Management Method of Low-Voltage Active Distribution Networks Based on Distribution Locational Marginal Price

Large-scale distributed energy resources, such as electric vehicles (EVs) asymmetric access to low-voltage distribution systems, may cause security problems, including line congestion, voltage violation, and three-phase unbalance. In this paper, a congestion management method of low-voltage active distribution networks is proposed. The soft open point, a flexible power electronic device, is considered as a direct control means to solve the congestion problem first. A semidefinite programming model based on a symmetrical component method is constructed to optimize the operation strategy that can be efficiently solved to meet the demands of rapid centralized control. To guide the charging behaviors of flexible load represented by EVs, a market mechanism suitable for the low-voltage unbalanced network is further considered. Though linear approximation and sensitivity analysis, a pricing model of flexible load is established accounting for the effect of network loss, voltage variation, voltage three-phase unbalance, and line overload to distribution locational marginal price. Case studies are carried out on the modified IEEE 33-node and IEEE 123-node system to verify the effectiveness and efficiency of the proposed method.

Distribution System Restructuring: Distribution LMP via Unbalanced ACOPF

The emerging distribution system with a proliferation of distributed energy resources and flexible demand assets is expected to experience a restructuring process, just as what has been happening in the transmission system. This paper introduces the distribution locational marginal price (DLMP) as an effectively economic signal to quantify marginal cost for supplying next incremental loads at different phases of individual nodes. DLMP is calculated by solving the unbalanced ac optimal power flow (ACOPF) problem of distribution systems, with the objective of minimizing system operation cost. Indeed, in order to derive effective DLMPs, global optimal solution to the non-convex unbalanced ACOPF problem with a zero duality gap needs to be obtained. This paper solves the unbalanced ACOPF problem via the moment relaxation-based semidefinite programming (SDP) model. System sparsity is explored to accelerate the computational performance. In addition, a hierarchical approach is proposed to recover a good enough feasible solution to the original ACOPF, when the sparse moment relaxation-based SDP model is inexact. Numerical case studies on a modified IEEE 34-bus system evaluate the effectiveness and validity of the proposed approach. DLMP-based revenue adequacy of the distribution system is also analyzed.

Analysis of Distribution Locational Marginal Prices

Low-voltage distribution networks are emerging as an increasingly important component of power system operations due to the deployment of distributed renewable resources (e.g., rooftop solar supply) and the need to mobilize the flexibility of consumers that are connected to the low-voltage grid. The pricing of electric power at distribution nodes follows directly from the theory of spot pricing of electricity. However, in contrast to linearized lossless models of transmission networks, an intuitive understanding of prices at the distribution level presents challenges due to voltage limits, reactive power flows, and losses. In this paper, we present three approaches toward understanding distribution locational marginal prices by decomposing them: 1) through a duality analysis of the problem formulated with a global power balance constraint; 2) through a duality analysis of a second-order cone program relaxation; and 3) through an analysis of the impact of marginal losses on price. We discuss the relative strengths and weaknesses of each approach in terms of computation and physical intuition, and demonstrate the concepts on a 15-bus radial distribution network.

Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support

In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method.

Novel Linearized Power Flow and Linearized OPF Models for Active Distribution Networks With Application in Distribution LMP

The locational marginal price (LMP) methodology has been discussed for distribution networks/systems under the smart grid initiative. In this paper, a new distribution LMP (DLMP) formulation is presented which includes reactive power prices and voltage constraints. To solve DLMP, three modeling tools, namely, linearized power flow for distribution (LPF-D), loss factors for distribution (LF-D), and linear optimal power flow for distribution (LOPF-D) are proposed. LPF-D solves not only voltage angles but also magnitudes through linear expression between bus injections and bus voltages, specifically for distribution systems. LF-D is solved recursively based on the radial topology of typical distribution systems. With the integration of LPF-D and LF-D, conventional optimal power flow (OPF) can be reformulated as LOPF-D which is essentially a linear programming model. Test results on various systems show that: 1) LPF-D efficiently yields very close results if compared with AC power flow; 2) LOPF-D provides very close dispatch results in both real and reactive power if compared with ACOPF; and 3) the proposed DLMPs calculated with LF-D and LOPF-D give accurate price information if compared with the prices from ACOPF. Further, these three tools are not limited to DLMP but can be potentially applied to other distribution analyses.

Transactive-Market-Based Operation of Distributed Electrical Energy Storage with Grid Constraints

In a transactive energy market, distributed energy resources (DERs) such as dispatchable distributed generators (DGs), electrical energy storages (EESs), distribution-scale load aggregators (LAs), and renewable energy sources (RESs) have to earn their share of supply or demand through a bidding process. In such a market, the distribution system operator (DSO) may optimally schedule these resources, first in a forward market, i.e., day-ahead, and in a real-time market later on, while maintaining a reliable and economic distribution grid. In this paper, an efficient day-ahead scheduling of these resources, in the presence of interaction with wholesale market at the locational marginal price (LMP), is studied. Due to inclusion of EES units with integer constraints, a detailed mixed integer linear programming (MILP) formulation that incorporates simplified DistFlow equations to account for grid constraints is proposed. Convex quadratic line and transformer apparent power flow constraints have been linearized using an outer approximation. The proposed model schedules DERs based on distribution locational marginal price (DLMP), which is obtained as the Lagrange multiplier of the real power balance constraint at each distribution bus while maintaining physical grid constraints such as line limits, transformer limits, and bus voltage magnitudes. Case studies are performed on a modified IEEE 13-bus system with high DER penetration. Simulation results show the validity and efficiency of the proposed model.

Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Through Chance Constrained Mixed-Integer Programming

This paper presents a distribution locational marginal pricing (DLMP) method through chance constrained mixed-integer programming (MIP) designed to alleviate the possible congestion in the future distribution network with high penetration of electric vehicles (EVs). In order to represent the stochastic characteristics of the EV driving patterns, a chance constrained optimization of the EV charging is proposed and formulated through MIP. With the chance constraints in the optimization formulations, it guarantees that the failure probability of the EV charging plan fulfilling the driving requirement is below the predetermined confidence parameter. The efficacy of the proposed approach was demonstrated by case studies using a 33-bus distribution system of the Bornholm power system and the Danish driving data. The case study results show that the DLMP method through chance constrained MIP can successfully alleviate the congestion in the distribution network due to the EV charging while keeping the failure probability of EV charging not meeting driving needs below the predefined confidence.

Distributed Congestion Management of Distribution Grids Under Robust Flexible Buildings Operations

Flexible demand side energy and reserve procurement have the potential to improve the overall operation of the grid. However, as argued in previous studies, this flexibility might cause congestion in distribution grids. In this paper, we improve the conventional distribution locational marginal price (DLMP) method, while integrating congestion free energy and reserve provision from buildings in distribution grids. First, robust day-ahead (DA) DLMPs are calculated to account for unmodeled dynamics of flexible loads. Second, using dual decomposition, the data sharing requirements between the aggregator and the distribution system operator are minimized. Third, a sensitivity-based real-time adjustment method is presented to remove the conservatism of DA robust DLMPs. Case studies are performed on a benchmark distribution system. The numerical results show that the proposed technique efficiently handles load uncertainties and data sharing requirements, improving the practicality of the conventional DMLP method.

Two stage residential energy management under distribution locational marginal pricing

This paper proposes a new optimization model for Smart Home Management Systems (SHMS) in order to increase the profits of Load Serve Entities (LSEs) and customers from technical and financial points of view. In the recent decades, performing Demand Response (DR) is one of the most efficient ways to improve the performance of power distribution systems in terms of power loss, and investment costs. The LSEs can implements some strategies like offering incentives to customers to change their consumption pattern with the aim of reducing power loss, improving asset management and increasing the profits. On the other hand, the end users can participate in DR programs to decrease electricity bills and earn monetary incentives from LSEs proportionate to their contributions to the energy loss reduction. In this paper, Distribution Locational Marginal Price (DLMP) instead of time-based pricing mechanism is applied to bill the customers. In the proposed strategy, the energy bill of customers and power loss of the system are simultaneously decreased. For dealing with uncertainties, stochastic variables computation module is designed which generates several scenarios by Monte Carlo simulation at each hour. The operation of household resources and appliances are optimized through a Mixed Integer Linear Programming (MILP), which has a two-stage stochastic model. The results explicitly show benefits of the proposed stochastic model since it provides accuracy in scheduling and decreases the operation cost. Besides, the superiority of DLMP and the proposed method over existing pricing mechanism is demonstrated.

Deterministic approach for active distribution networks planning with high penetration of wind and solar power

In this paper, a novel deterministic approach for the planning of active distribution networks within a distribution market environment considering multi-configuration of wind turbines (WTs) and photovoltaic (PV) cells is proposed. Multi-configuration multi-period market-based optimal power flow is utilized for maximizing social welfare taking into account uncertainties associated with wind speed, solar irradiance and load demand as well as different operational status of WTs and PVs. Multi-period scenarios method is exploited to model the aforementioned uncertainties. The proposed approach assesses the effect of multiple-configuration of PVs and WTs on the amount of wind and solar power that can be produced, the distribution locational marginal prices all over the network and on the social welfare. The application of the proposed approach is examined on a 30-bus radial distribution network.

Evaluation Methodology for Tariff Design under Escalating Penetrations of Distributed Energy Resources

As the penetration of distributed energy resources (DERs) escalates in distribution networks, new network tariffs are needed to cope with this new situation. These tariffs should allocate network costs to users, promoting an efficient use of the distribution network. This paper proposes a methodology to evaluate and compare network tariff designs. Four design attributes are proposed for this aim: (i) network cost recovery; (ii) deferral of network reinforcements; (iii) efficient consumer response; (iv) recognition of side-effects on consumers. Through an analytical hierarchy process (AHP), the evaluation methodology is applied to compare traditional cost allocation methods, on the basis of 100% energy, 100% demand, and 50% energy-50% demand, with more advanced pricing methods based on distribution locational marginal prices in combination with cost-reflective network charges. Numerical results are obtained through a case study based on the IEEE 34-node test feeder with DER integration. The results illustrate the advantage of advanced pricing methods to promote an efficient integration of DER and demand price-response from consumers.

Cost Optimal Integration of Flexible Buildings in Congested Distribution Grids

Buildings are candidates for providing flexible demand due to their high consumption and inherent thermal inertia. In the future, flexible demand side reserves may also help to relax the expected higher reserve requirements of the grid due to the presence of renewables. However, this flexible demand might be vulnerable to price signals, as the simultaneous increase in consumption by multiple buildings due to low (high) energy (reserves) price periods might cause congestion in distribution grids. In order to integrate congestion-free energy and reserve provision from buildings, this paper presents two benchmark pricing methodologies: (1 ) distribution locational marginal prices (DLMP), and (2) iterative DLMP (iDLMP). Both methods deploy convex optimization to obtain an optimal solution of the original problem. Using dual decomposition, a settlement scheme, which efficiently distributes the congestion cost among involved participants, is also presented. Case studies are performed on a benchmark distribution system along with the National Energy Market Singapore's price framework. The results prove that both methods optimally remove congestion from distribution grids and have potential to be integrated into the theoretical framework of liberalized markets. Furthermore, as a comparison, it is shown that the DLMP-based prices outperforms existing pricing structures of the distribution grid. Hence, using this scheme, the distribution system operator can evaluate existing tariffs and introduce incentives for price responsive demands. However, to support these methods, the high requirement for information sharing in the DLMP method and/or communication technology infrastructure for calculating iDLMPs must exist in the future grid.

Distribution Locational Marginal Pricing by Convexified ACOPF and Hierarchical Dispatch

This paper proposes a hierarchical economic dispatch (HED) mechanism for computing distribution locational marginal prices (DLMPs). The HED mechanism involves three levels: 1) the top level is the national (regional) transmission network; 2) the middle level is the distribution network; and while 3) the lowest level reflects local embedded networks or microgrids. Each network operator communicates its generalized bid functions (GBFs) to the next higher level of the hierarchy. The GBFs approximate the true cost function of a network by a series of affine functions. The concept of Benders cuts are employed in simulating the GBFs. The ac optimal power flow (ACOPF) is convexified and then used for dispatching generators and calculating GBFs and DLMPs. The proposed convexification is based on the second order cone reformulation. A sequential optimization algorithm is developed to tighten the proposed second order cone relaxation of ACOPF. The properties of the sequential tightness algorithm are discussed and proved. The HED is implemented in the GAMS grid computing platform. The GBFs and DLMPs are calculated for the modified IEEE 342 node low voltage test system. The numerical results show the utility of the proposed HED and GBF in implementing DLMP.

Marketing equilibria of integrated heating and power system considering locational marginal pricing in distribution networks

The marketing equilibria investigation contributes to the economic operation of the district integrated heating and power system. The locational marginal price (LMP) of distribution generations interacts with the load. This study introduced game theory to investigate the Nash equilibria in the integrated heating and power system. The optimal operation strategy can be efficiently obtained by introducing the demand response of LMP. In this study, a distribution LMP (DLMP) model is formulated considering non-linear power flow constrains. The heating system topology is specified to calculate the heat flow. The demand response of DLMP is defined with an influence coefficient to procure the convergence of the iterative process. The Nash equilibria are obtained. The simulation results are discussed to prove the feasibility and efficiency of the models and algorithms, and sensitivity analysis of the influence coefficient is illustrated.

Active distribution networks planning with high penetration of wind power

In this paper, a stochastic method for active distribution networks planning within a distribution market environment considering multi-configuration of wind turbines is proposed. Multi-configuration multi-scenario market-based optimal power flow is used to maximize the social welfare considering uncertainties related to wind speed and load demand and different operational status of wind turbines (multiple-wind turbine configurations). Scenario-based approach is used to model the abovementioned uncertainties. The method evaluates the impact of multiple-wind turbine configurations and active network management schemes on the amount of wind power that can be injected into the grid, the distribution locational marginal prices throughout the network and on the social welfare. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system. It was shown that multi-wind turbine configurations under active network management schemes, including coordinated voltage control and adaptive power factor control, can increase the amount of wind power that can be injected into the grid; therefore, the distribution locational marginal prices reduce throughout the network significantly.

Co-Optimization of Power and Reserves in Dynamic T&D Power Markets With Nondispatchable Renewable Generation and Distributed Energy Resources

Marginal-cost-based dynamic pricing of electricity services, including real power, reactive power, and reserves, may provide unprecedented efficiencies and system synergies that are pivotal to the sustainability of massive renewable generation integration. Extension of wholesale high-voltage power markets to allow distribution network connected prosumers to participate, albeit desirable, has stalled on high transaction costs and the lack of a tractable market clearing framework. This paper presents a distributed, massively parallel architecture that enables tractable transmission and distribution locational marginal price (TD electric vehicle (EV) battery charging and storage; heating, ventilating, and air conditioning (HVAC) and combined heat & power (CHP) microgenerators; computing; volt/var control devices; grid-friendly appliances; smart transformers; and more. The proposed iterative distributed architecture can discover T&DLMPs while capturing the full complexity of each participating DER’s intertemporal preferences and physical system dynamics.

A novel method for evaluating the impact of residential demand response in a real time distribution energy market

This paper provides a new methodology for assessing the effect on both residential users and distribution networks of the introduction of demand response initiatives based a novel transactive controller. The proposed controller considers forecasted load peaks and distribution locational marginal prices in a real time distribution market. Monte Carlo simulation is used to consider the stochastic variations of the involved variables and assess the impact of the proposed method. Simulations results, carried out over 1 month in winter and considering a 74-buses distribution network, demonstrated that (1) residential end-users can achieve cost savings and (2) the reliability of the distribution network can be improved.