Multi-area economic dispatch (MAED) provides an indispensable component for the security and economic operation of contemporary power systems. Over recent years, numerous metaheuristic optimization algorithms (MOAs) have surfaced for addressing the MAED problem. However, none of the literature to date conducted a comprehensive statistical research work on the MAED problem. In part I of this series, we present a comprehensive survey on this problem. (1) We collect all eleven reported MAED cases studied over the years. These cases have different structures, scales, and constraints. We illustrate the structures of all cases and provide their corresponding system parameters. (2) We collect all the MOA solution algorithms. These algorithms are inspired by different ways, and we categorize them in detail and review them comprehensively. (3) We list the detailed applications of MOAs on different cases and count the percentage of studies on each case. (4) Finally, we summarize the current research progress and point out the future research directions in terms of MAED models and solution methods, respectively. This survey provides an extensive overview of the MAED cases and its solution methods. It can provide applicable and reference suggestions for future research on the MAED problem.

Electric vehicles (EVs), as a new type of dispatchable resource in distribution networks, can help local new energy consumption and enhance grid security. In this paper, a method for evaluating and optimizing the dispatching of adjustable resources of electric vehicles considering cross-station mutual aid is proposed. Firstly, a Long Short-Term Memory (LSTM) network model is used to predict the power demand of charging posts and the number of Evs, then a consumer psychology model is constructed to evaluate the adjustable resources of EVs by considering the extra time and cost factors of cross-station charging. Then a scheduling model considering cross-station support for EVs is built. Through the case study, the effectiveness of the method in guaranteeing grid security and enhancing the level of new energy consumption is verified.

This paper proposes a Transformer-based processor supporting energy-efficient fine-tuning with batch-iteration-matrix multi-level optimizations. It has three key features: 1) An exponent-stationary re-computing scheduler (ESRC) reduces 44.2% of the storage requirement for each batch. 2) An aggressive linear fitting unit (ALFU) saves 47.4% of the computations in each iteration. 3) A logarithmic domain processing element (LDPE) decreases 36.3% of energy for matrix multiplications (MM) in fine-tuning. The proposed Transformer processor achieves an energy efficiency of 54.94TFLOPS/W. It reduces fine-tuning energy by 4.27× and offers 3.57× speedup for GPT-2.

This letter proposes a novel framework for modeling the response-price relationship of intertemporally responsive loads (IRL) using historical data. This task is cast as a data-driven inverse optimization (DDIO) problem, which trains a surrogate model whose best response to electricity price most closely resembles the observed power trajectory of IRLs. The virtual battery fleet with an adjustable number of elements is used as the surrogate model, which yields a linear modeling result. The DDIO is a bilevel programming problem. To solve it efficiently, a Newton-based algorithm with a grid fitting initialization technique is developed. The accuracy and robustness of the proposed modeling method are validated by numerical tests in comparison with other machine learning regressors.

This paper studies the optimal operation of integrated energy system and the participation mechanism of demand side response, establishes the operation optimization model of integrated energy system, and puts forward the transaction strategy formulation process of participating in demand side response. Finally, an example is given to verify that the proposed transaction mechanism and operation optimization model can promote the integrated energy system to participate in demand response, improve the system new energy consumption level and reduce the system operation cost.

The condition of resource distribution and power consumption differs between provinces. Resource will be wasted when dispatched only within province, and trans-province dispatch will be an ideal solution to optimize the allocation of resources. A trans-province security green dispatch pattern, which collaborates with province dispatch, is proposed. Trans-province optimal resource dispatch is attained to improve the new energy accommodation and mutual power aid between provinces. Based on dispatch model, trans-province security green index is designed and used to guide the optimization, and then assess the optimization effect. Taking simplified model of an area power grid for example, the simulation result shows that the proposal method is feasible and effective. The safety level and new energy accommodation of area power grid are obviously improved through trans-province dispatch.

This paper proposes a new decomposition method for the security-constrained economic dispatch in a three-layer large-scale power system. The decomposition is realized using two main techniques. The first is to use Ward equivalencing-based network reduction to reduce the number of variables and constraints in the high-layer model without sacrificing accuracy. The second is to develop a price response function to exchange signal information between neighboring layers, which significantly improves the information exchange efficiency of each iteration and results in less iterations and less computational time. The case studies based on the duplicated RTS-79 system demonstrate the effectiveness and robustness of the proposed method.

High variability of renewable generations poses significant challenges to the secure and economic operation of power systems, especially in terms of ramp scarcity. This paper introduces a multi-area look-ahead coordination framework for real-time dispatch, which guarantees that each region has sufficient ramping capability through a cross-area sharing of ramp resources. This approach allows for a more reliable and economic operation with high renewable variabilities. In the proposed framework, each area dispatch center conducts its own look-ahead optimization with exchanged information about the boundary states and marginal costs of their neighboring areas. Power exchanges are scheduled to deliver ramping supports to areas with large net load variations in the upcoming time steps. A dynamic multiplier-based algorithm is employed for solving the multi-area look-ahead model, which is fully decentralized and thus preserves autonomy for each area. The dynamic multiplier-based algorithm presents the advantage of accelerated computation and better robustness when dealing with stringent ramp constraints. Numerical examples in a 6-bus system illustrate the benefits of conducting multi-area look-ahead coordination.

If partial maintenance is permitted for those maintenance that is not urgent, it will help alleviate emergency conditions through proper restoration strategy. This letter presents a corrective short-term transmission maintenance scheduling model considering post-contingency restoration of some maintenance transmission lines (STMS-PCR), which allows re-scheduling of the maintenance schedules after contingencies. Numerical tests based on six-bus system show that the STMS-PCR method improves the economics and reliability of power system operations.

This paper proposes a short-term load forecasting method based on load decomposition and numerical weather forecast. Load is decomposed into two components: the base component and the weather-sensitive component, which are forecasted separately. The Seasonal and Trend decomposition by Loess (STL) algorithm is adopted in load decomposition, allowing the base component to change over time. The Holt-Winters model is adopted in forecasting time series of the base component. A Support Vector Regression (SVR) model is trained by historical load data and meteorological data in forecasting the weather-sensitive component. The proposed method is tested using the real electricity load data of a city grid in South China. Case study shows the effectiveness of the proposed methodology.

The integrated architecture of generation and retail is common in a variety of places around the world. The pricing strategy is extremely important to retailers, which significantly affects their profit. ToU and RTP are lack of individuation and the price signal of average cost per kWh is not straightforward to customers. The contribution of a customer's load profile to a generator's utilization rate should be fully considered in retailers' pricing strategy. Therefore, in this paper, a general method to quantitatively evaluate the contribution of a customer's load profile to a generator's utilization rate is firstly proposed. The system or regional typical load profile is chosen as the benchmark. The pricing strategy of retailers with generation assets in retail market based on load profile is then proposed. The load profile-based pricing can provide incentives to customers to optimize their consumption behaviors so that the utilization rate of the generator and the profit of the retailer will increase. Case study indicates that the proposed pricing strategy is feasible and fair to all the market participants.

This study presents a closed-loop coordination mode and model between generation maintenance scheduling (GMS) and long-term security-constrained unit commitment (SCUC) considering energy constraints and N − 1 contingencies that aims at improving the security and economy of power system operations. Given the calculation complexity, this study employs constraint transformation techniques and an efficient approach termed the relaxation induced method, which is based on the solution of the relaxed mixed-integer programming (MIP) model. The proposed approach can quickly obtain a near-optimal solution. If the near-optimal solution is not acceptable for system operators, it can be used to warm-start the solution of the original MIP problem. The modified IEEE 30-bus test system and a provincial power system in China are employed to demonstrate the effectiveness of the proposed model and algorithm.

Generation maintenance scheduling (GMS) plays an important role in power system operations. The restructuring of the power industry has forced changes to the traditional maintenance mechanism. On one hand, the generation companies seek to maximize their profit. On the other hand, the independent system operator (ISO) strives to maintain the operational reliability of the system while maximizing the social welfare. This paper proposes a coordination mechanism for generation maintenance scheduling in electricity markets. In order to solve the resulting large mixed integer programming (MIP) problem, a relaxation induced algorithm is utilized. This technique is based on the solution of the linear relaxed problem. The features of the coordination mechanism and the performance of the algorithm are demonstrated using the IEEE-118 bus system and a provincial power system in China. Case studies show that the proposed mechanism not only ensures the maintenance preference of the generating companies, but also maintains the operational reliability of the system. They also demonstrate that the algorithm is quite efficient at solving the optimization problem.

This paper presents an approach for integrated generation and transmission maintenance scheduling model (IMS) that takes into consideration N-1 contingencies. The objective is to maximize the maintenance preference of facility owners while satisfying N-1 security and other constraints. To achieve this goal, Benders decomposition is employed to decompose the problem into a master problem and several sub-problems. A Relaxation Induced (RI) algorithm is proposed to efficiently solve the large mixed integer programming (MIP) master problem. This algorithm is based on the solution of the linear relaxed problem. It is demonstrated that the proposed algorithm can efficiently reach a near-optimal solution that is usually satisfactory. If this near-optimal solution is not acceptable, it is used as the initial solution to fast start the solution of the original IMS problem. The performance of the proposed method is demonstrated using a modified version of the IEEE 30-bus system and a model of the power system of a Chinese province. Case studies show that the proposed algorithm can improve the computational efficiency by more than an order of magnitude.

Optimal transmission switching (OTS) exploits the flexibility in grid topology to reduce the system dispatch cost. However, DC-power-flow-based OTS solution cannot guarantee a feasible AC dispatch, which is one of the challenges in practical implementation. To bridge the gap between the theoretical basis and practical implementation of OTS, this paper proposes a conic programming approach to the OTS problem. A mixed integer second order cone programming model is formulated to allow for incorporating reactive power and voltage security constraints, which significantly improves the AC feasibility of the OTS solution. The efficacy of the proposed model is illustrated in the IEEE 57-bus system.

Generation maintenance scheduling (GMS) is vital for the reliable and economical operation of a power system. With the increase in electricity demand, the generation reserve margin is decreased. In competitive electricity markets, the coordination of the conflicts among GENCOs' proposed outage schedules challenges the system operators. This paper proposes that demand side flexibility can be utilized to achieve a better solution. Load shifting costs are used to express users' willingness to shift their loads. A coordination mode between GENCOs and shiftable loads and a compensatory mechanism for shiftable loads are proposed in this paper. On this basis, the GMS model with shiftable loads is structured as a mixed integer programming (MIP) and mixed integer quadratic programming (MIQP) problem that can be solved by commercial solvers CPLEX. A modified IEEE 30-bus case is utilized for detailed analysis. Numerical results show that the proposed model is effective and the shiftable load can improve both the economy and reliability of GMS.

The volatility and intermittence of wind power brings a great challenge to the traditional security constrained economic dispatch(SCED).To dispatch the hydro power,thermal power and wind power coordinately,a full-scenario SCED method with coordinative optimization of hydro-thermal-wind power was proposed.By using interval numbers to cover the range of wind power fluctuation,the method unified the optimization of power output of traditional generation units and their spinning reserve for wind power fluctuation.Meanwhile,different ramp capability of hydro and thermal units was considered so that spinning reserve was allocated fairly and economically.Since the original model with interval numbers is complicated to solve,an identification method for critical scenarios was proposed.By the identification of critical scenarios,the original model was simplified based on critical scenario constraints without loss of accuracy so that the coordinative optimization was accelerated.Based on the practical data of a provincial power grid,the numerical experiment results show that the proposed method is effective and practical.

In response to the challenges brought on by the energy conservation based generation dispatch (ECGD) in China, a dynamic economic dispatch model that considers transmission losses (DED-TL) and a new solution framework are proposed to improve the accuracy, efficiency, and robustness of generation scheduling. Transmission losses are presented explicitly in the model so that the total energy consumption, consisting of both the generators' operation costs and also the transmission losses, can be well considered. The model is formulated as a quadratically constrained quadratic program (QCQP) problem that can be solved by commercial solvers. A penalty-based algorithm is proposed to address the virtual transmission loss problem brought on by negative nodal marginal costs. Three cases-a 6-bus system, the IEEE 30-bus system, and a 2746-bus system based on the electric energy system of Poland-are employed in numerical experiments to demonstrate the effectiveness of the proposed method. The results show that the better scheduling results in higher accuracy, and that good computational efficiency can be acquired via the proposed method.

To improve the capacity of RBF neural network and make short-term load forecasting more accurate and faster,a neural network ant colony optimization algorithm and Radial Basis Function neural network forecasting model is established by using the ant colony optimization algorithm to train the RBF neural network.Using the method and history load data of shanxi power system,the short-term load forecasting was carried out.The simulation results show that the forecasting results by the proposed method are better than those by RBF neural network method.The optimization algorithm improves the RBF neural network generation capacity,and the short-term load forecasting accuracy is improved in Shanxi power system.So it can be effectively used in short-term load forecasting of power system.